| 1 |
#' Enhanced Function Audit System |
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| 2 |
#' |
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| 3 |
#' @description Comprehensive function audit and categorization system for Issue #393 |
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#' @keywords internal |
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#' @noRd |
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| 6 | ||
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#' Audit all exported functions |
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#' |
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#' @return Comprehensive function audit results |
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audit_all_functions <- function() {
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# Get all exported functions |
|
| 12 | ! |
exported_functions <- get_exported_functions() |
| 13 | ||
| 14 | ! |
cat("š Starting comprehensive function audit...\n")
|
| 15 | ! |
cat("š Total functions to audit:", length(exported_functions), "\n\n")
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| 16 | ||
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# Analyze each function |
|
| 18 | ! |
function_analysis <- list() |
| 19 | ! |
for (i in seq_along(exported_functions)) {
|
| 20 | ! |
func_name <- exported_functions[i] |
| 21 | ! |
cat("š Analyzing function", i, "of", length(exported_functions), ":", func_name, "\n")
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| 22 | ||
| 23 | ! |
function_analysis[[func_name]] <- analyze_function(func_name) |
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} |
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| 25 | ||
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# Categorize functions |
|
| 27 | ! |
categories <- categorize_functions(function_analysis) |
| 28 | ||
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# Generate audit report |
|
| 30 | ! |
audit_report <- generate_audit_report(categories, function_analysis) |
| 31 | ||
| 32 | ! |
cat("\nā
Function audit completed successfully!\n")
|
| 33 | ! |
return(audit_report) |
| 34 |
} |
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| 35 | ||
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#' Get all exported functions from NAMESPACE |
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#' |
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#' @return Vector of exported function names |
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| 39 |
get_exported_functions <- function() {
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# Read NAMESPACE file |
|
| 41 | ! |
namespace_lines <- readLines("NAMESPACE")
|
| 42 | ! |
export_lines <- grep("^export\\(", namespace_lines, value = TRUE)
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| 43 | ||
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# Extract function names |
|
| 45 | ! |
function_names <- gsub("^export\\(([^)]+)\\)", "\\1", export_lines)
|
| 46 | ! |
function_names <- gsub('"', "", function_names)
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| 47 | ||
| 48 |
# Remove magrittr pipe operator |
|
| 49 | ! |
function_names <- function_names[function_names != "%>%"] |
| 50 | ||
| 51 | ! |
return(function_names) |
| 52 |
} |
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| 53 | ||
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#' Analyze individual function |
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#' |
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#' @param function_name Name of function to analyze |
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#' @return Function analysis results |
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analyze_function <- function(function_name) {
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# Get function signature |
|
| 60 | ! |
signature <- get_function_signature(function_name) |
| 61 | ||
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# Get function documentation |
|
| 63 | ! |
documentation <- get_function_documentation(function_name) |
| 64 | ||
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# Analyze function usage |
|
| 66 | ! |
usage <- analyze_function_usage(function_name) |
| 67 | ||
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# Get function dependencies |
|
| 69 | ! |
dependencies <- get_function_dependencies(function_name) |
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# Get function file location |
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| 72 | ! |
file_location <- get_function_file_location(function_name) |
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# Analyze function complexity |
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| 75 | ! |
complexity <- analyze_function_complexity(function_name) |
| 76 | ||
| 77 | ! |
return(list( |
| 78 | ! |
name = function_name, |
| 79 | ! |
signature = signature, |
| 80 | ! |
documentation = documentation, |
| 81 | ! |
usage = usage, |
| 82 | ! |
dependencies = dependencies, |
| 83 | ! |
file_location = file_location, |
| 84 | ! |
complexity = complexity |
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)) |
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} |
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| 87 | ||
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#' Get function signature |
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#' |
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#' @param function_name Name of function |
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#' @return Function signature |
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get_function_signature <- function(function_name) {
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| 93 | ! |
tryCatch( |
| 94 |
{
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| 95 | ! |
func <- get(function_name, envir = asNamespace("zoomstudentengagement"))
|
| 96 | ! |
if (is.function(func)) {
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| 97 | ! |
args <- formals(func) |
| 98 | ! |
return(paste(names(args), collapse = ", ")) |
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} else {
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| 100 | ! |
return("Not a function")
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} |
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}, |
|
| 103 | ! |
error = function(e) {
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| 104 | ! |
return("Error retrieving signature")
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} |
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) |
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} |
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#' Get function documentation |
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#' |
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#' @param function_name Name of function |
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#' @return Documentation status |
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get_function_documentation <- function(function_name) {
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# Check if function has roxygen2 documentation |
|
| 115 | ! |
man_file <- file.path("man", paste0(function_name, ".Rd"))
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| 116 | ||
| 117 | ! |
if (file.exists(man_file)) {
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| 118 | ! |
return("Complete")
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} else {
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| 120 | ! |
return("Missing")
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} |
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} |
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#' Analyze function usage patterns |
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#' |
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#' @param function_name Name of function |
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#' @return Usage analysis |
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analyze_function_usage <- function(function_name) {
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|
| 129 | ! |
usage_info <- list( |
| 130 | ! |
in_vignettes = FALSE, |
| 131 | ! |
in_examples = FALSE, |
| 132 | ! |
in_tests = FALSE, |
| 133 | ! |
usage_count = 0 |
| 134 |
) |
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| 135 | ||
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# Check vignettes |
|
| 137 | ! |
vignette_files <- list.files("vignettes", pattern = "\\.Rmd$", full.names = TRUE)
|
| 138 | ! |
for (vignette in vignette_files) {
|
| 139 | ! |
if (file.exists(vignette)) {
|
| 140 | ! |
content <- readLines(vignette, warn = FALSE) |
| 141 | ! |
if (any(grepl(function_name, content, fixed = TRUE))) {
|
| 142 | ! |
usage_info$in_vignettes <- TRUE |
| 143 | ! |
usage_info$usage_count <- usage_info$usage_count + 1 |
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} |
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} |
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} |
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| 147 | ||
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# Check examples in man files |
|
| 149 | ! |
man_file <- file.path("man", paste0(function_name, ".Rd"))
|
| 150 | ! |
if (file.exists(man_file)) {
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| 151 | ! |
content <- readLines(man_file, warn = FALSE) |
| 152 | ! |
if (any(grepl("\\\\examples", content))) {
|
| 153 | ! |
usage_info$in_examples <- TRUE |
| 154 | ! |
usage_info$usage_count <- usage_info$usage_count + 1 |
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} |
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} |
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| 157 | ||
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# Check tests - look for function name in test files |
|
| 159 | ! |
all_files <- list.files("tests/testthat", full.names = TRUE)
|
| 160 | ! |
test_files <- all_files[grepl("^test-.*\\.R$", basename(all_files))]
|
| 161 | ! |
for (test_file in test_files) {
|
| 162 | ! |
if (file.exists(test_file)) {
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| 163 | ! |
content <- readLines(test_file, warn = FALSE) |
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# Look for function name in various contexts (calls, test names, etc.) |
|
| 165 | ! |
if (any(grepl(function_name, content, fixed = TRUE)) || |
| 166 | ! |
any(grepl(paste0("test.*", function_name), content, ignore.case = TRUE))) {
|
| 167 | ! |
usage_info$in_tests <- TRUE |
| 168 | ! |
usage_info$usage_count <- usage_info$usage_count + 1 |
| 169 | ! |
break # Found in at least one test file |
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} |
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} |
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} |
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| 173 | ||
| 174 | ! |
return(usage_info) |
| 175 |
} |
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| 176 | ||
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#' Get function dependencies |
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#' |
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#' @param function_name Name of function |
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#' @return Function dependencies |
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get_function_dependencies <- function(function_name) {
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|
| 182 | ! |
tryCatch( |
| 183 |
{
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|
| 184 | ! |
func <- get(function_name, envir = asNamespace("zoomstudentengagement"))
|
| 185 | ! |
if (is.function(func)) {
|
| 186 |
# Get function body |
|
| 187 | ! |
body_text <- deparse(body(func)) |
| 188 | ||
| 189 |
# Find function calls |
|
| 190 | ! |
function_calls <- character(0) |
| 191 | ! |
for (line in body_text) {
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# Simple regex to find function calls |
|
| 193 | ! |
calls <- regmatches(line, gregexpr("\\b[a-zA-Z_][a-zA-Z0-9_.]*\\s*\\(", line))[[1]]
|
| 194 | ! |
calls <- gsub("\\s*\\(", "", calls)
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| 195 | ! |
function_calls <- c(function_calls, calls) |
| 196 |
} |
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| 197 | ||
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# Remove duplicates and common R functions |
|
| 199 | ! |
function_calls <- unique(function_calls) |
| 200 | ! |
common_functions <- c( |
| 201 | ! |
"if", "for", "while", "return", "stop", "warning", "message", |
| 202 | ! |
"cat", "print", "paste", "paste0", "c", "list", "data.frame", |
| 203 | ! |
"as.data.frame", "as.character", "as.numeric", "length", "nrow", "ncol" |
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) |
|
| 205 | ! |
function_calls <- setdiff(function_calls, common_functions) |
| 206 | ||
| 207 | ! |
return(function_calls) |
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} else {
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| 209 | ! |
return(character(0)) |
| 210 |
} |
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}, |
|
| 212 | ! |
error = function(e) {
|
| 213 | ! |
return(character(0)) |
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} |
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) |
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} |
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| 217 | ||
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#' Get function file location |
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#' |
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#' @param function_name Name of function |
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#' @return File location |
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get_function_file_location <- function(function_name) {
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# Search through R files |
|
| 224 | ! |
r_files <- list.files("R", pattern = "\\.R$", full.names = TRUE)
|
| 225 | ||
| 226 | ! |
for (r_file in r_files) {
|
| 227 | ! |
if (file.exists(r_file)) {
|
| 228 | ! |
content <- readLines(r_file, warn = FALSE) |
| 229 |
# Look for function definition |
|
| 230 | ! |
func_pattern <- paste0("^", function_name, "\\s*<-\\s*function")
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| 231 | ! |
if (any(grepl(func_pattern, content))) {
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| 232 | ! |
return(basename(r_file)) |
| 233 |
} |
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} |
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} |
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| 236 | ||
| 237 | ! |
return("Unknown")
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} |
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#' Analyze function complexity |
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#' |
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#' @param function_name Name of function |
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#' @return Complexity analysis |
|
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analyze_function_complexity <- function(function_name) {
|
|
| 245 | ! |
tryCatch( |
| 246 |
{
|
|
| 247 | ! |
func <- get(function_name, envir = asNamespace("zoomstudentengagement"))
|
| 248 | ! |
if (is.function(func)) {
|
| 249 | ! |
body_text <- deparse(body(func)) |
| 250 | ||
| 251 | ! |
complexity <- list( |
| 252 | ! |
lines_of_code = length(body_text), |
| 253 | ! |
has_loops = any(grepl("for\\s*\\(|while\\s*\\(", body_text)),
|
| 254 | ! |
has_conditionals = any(grepl("if\\s*\\(|else", body_text)),
|
| 255 | ! |
has_error_handling = any(grepl("tryCatch|stop|warning", body_text)),
|
| 256 | ! |
function_calls = length(grep("\\w+\\s*\\(", body_text))
|
| 257 |
) |
|
| 258 | ||
| 259 | ! |
return(complexity) |
| 260 |
} else {
|
|
| 261 | ! |
return(list( |
| 262 | ! |
lines_of_code = 0, has_loops = FALSE, has_conditionals = FALSE, |
| 263 | ! |
has_error_handling = FALSE, function_calls = 0 |
| 264 |
)) |
|
| 265 |
} |
|
| 266 |
}, |
|
| 267 | ! |
error = function(e) {
|
| 268 | ! |
return(list( |
| 269 | ! |
lines_of_code = 0, has_loops = FALSE, has_conditionals = FALSE, |
| 270 | ! |
has_error_handling = FALSE, function_calls = 0 |
| 271 |
)) |
|
| 272 |
} |
|
| 273 |
) |
|
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} |
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| 275 | ||
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#' Generate comprehensive audit report |
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| 277 |
#' |
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#' @param categories Function categories |
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| 279 |
#' @param function_analysis Function analysis results |
|
| 280 |
#' @return Audit report |
|
| 281 |
generate_audit_report <- function(categories, function_analysis) {
|
|
| 282 | ! |
cat("\nš GENERATING COMPREHENSIVE AUDIT REPORT\n")
|
| 283 | ! |
cat(paste(rep("=", 50), collapse = ""), "\n\n")
|
| 284 | ||
| 285 |
# Summary statistics |
|
| 286 | ! |
total_functions <- length(function_analysis) |
| 287 | ! |
documented_functions <- sum(sapply(function_analysis, function(x) x$documentation == "Complete")) |
| 288 | ! |
functions_with_examples <- sum(sapply(function_analysis, function(x) x$usage$in_examples)) |
| 289 | ! |
functions_in_tests <- sum(sapply(function_analysis, function(x) x$usage$in_tests)) |
| 290 | ||
| 291 | ! |
cat("š SUMMARY STATISTICS\n")
|
| 292 | ! |
cat(paste(rep("-", 20), collapse = ""), "\n")
|
| 293 | ! |
cat("Total exported functions:", total_functions, "\n")
|
| 294 | ! |
cat( |
| 295 | ! |
"Functions with documentation:", documented_functions, "(",
|
| 296 | ! |
round(100 * documented_functions / total_functions, 1), "%)\n" |
| 297 |
) |
|
| 298 | ! |
cat( |
| 299 | ! |
"Functions with examples:", functions_with_examples, "(",
|
| 300 | ! |
round(100 * functions_with_examples / total_functions, 1), "%)\n" |
| 301 |
) |
|
| 302 | ! |
cat( |
| 303 | ! |
"Functions with tests:", functions_in_tests, "(",
|
| 304 | ! |
round(100 * functions_in_tests / total_functions, 1), "%)\n\n" |
| 305 |
) |
|
| 306 | ||
| 307 |
# Category breakdown |
|
| 308 | ! |
cat("š FUNCTION CATEGORIES\n")
|
| 309 | ! |
cat(paste(rep("-", 20), collapse = ""), "\n")
|
| 310 | ! |
for (category in names(categories)) {
|
| 311 | ! |
count <- length(categories[[category]]) |
| 312 | ! |
cat(sprintf("%-20s: %2d functions\n", category, count))
|
| 313 |
} |
|
| 314 | ! |
cat("\n")
|
| 315 | ||
| 316 |
# Detailed function list by category |
|
| 317 | ! |
for (category in names(categories)) {
|
| 318 | ! |
if (length(categories[[category]]) > 0) {
|
| 319 | ! |
cat("š", toupper(category), "FUNCTIONS\n")
|
| 320 | ! |
cat(paste(rep("-", nchar(category) + 10), collapse = ""), "\n")
|
| 321 | ! |
for (func_name in categories[[category]]) {
|
| 322 | ! |
func_info <- function_analysis[[func_name]] |
| 323 | ! |
doc_status <- if (func_info$documentation == "Complete") "ā " else "ā" |
| 324 | ! |
test_status <- if (func_info$usage$in_tests) "ā " else "ā" |
| 325 | ! |
example_status <- if (func_info$usage$in_examples) "ā " else "ā" |
| 326 | ||
| 327 | ! |
cat(sprintf( |
| 328 | ! |
" %-30s | Doc:%s Test:%s Ex:%s | %s\n", |
| 329 | ! |
func_name, doc_status, test_status, example_status, |
| 330 | ! |
func_info$file_location |
| 331 |
)) |
|
| 332 |
} |
|
| 333 | ! |
cat("\n")
|
| 334 |
} |
|
| 335 |
} |
|
| 336 | ||
| 337 |
# Create report object |
|
| 338 | ! |
report <- list( |
| 339 | ! |
summary = list( |
| 340 | ! |
total_functions = total_functions, |
| 341 | ! |
documented_functions = documented_functions, |
| 342 | ! |
functions_with_examples = functions_with_examples, |
| 343 | ! |
functions_in_tests = functions_in_tests |
| 344 |
), |
|
| 345 | ! |
categories = categories, |
| 346 | ! |
function_analysis = function_analysis, |
| 347 | ! |
generated_at = Sys.time() |
| 348 |
) |
|
| 349 | ||
| 350 | ! |
return(report) |
| 351 |
} |
|
| 352 | ||
| 353 |
#' Save audit report to file |
|
| 354 |
#' |
|
| 355 |
#' @param audit_report Audit report object |
|
| 356 |
#' @param filename Output filename |
|
| 357 |
save_audit_report <- function(audit_report, filename = "function_audit_report.rds") {
|
|
| 358 | ! |
saveRDS(audit_report, filename) |
| 359 | ! |
cat("š¾ Audit report saved to:", filename, "\n")
|
| 360 |
} |
|
| 361 | ||
| 362 |
#' Load audit report from file |
|
| 363 |
#' |
|
| 364 |
#' @param filename Input filename |
|
| 365 |
#' @return Audit report object |
|
| 366 |
load_audit_report <- function(filename = "function_audit_report.rds") {
|
|
| 367 | ! |
if (file.exists(filename)) {
|
| 368 | ! |
return(readRDS(filename)) |
| 369 |
} else {
|
|
| 370 | ! |
stop("Audit report file not found: ", filename)
|
| 371 |
} |
|
| 372 |
} |
| 1 |
#' Validate Ideal Course Transcript Structure |
|
| 2 |
#' |
|
| 3 |
#' Validates the structure and format of ideal course transcripts to ensure they |
|
| 4 |
#' meet expected standards for VTT format, file structure, and required fields. |
|
| 5 |
#' |
|
| 6 |
#' @param transcript_data Data frame containing transcript data (optional) |
|
| 7 |
#' @param file_path Path to transcript file (optional) |
|
| 8 |
#' @param strict_mode Logical. Whether to use strict validation rules. Default: TRUE |
|
| 9 |
#' @return Validation results list with status, issues, and recommendations |
|
| 10 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 11 |
#' @examples |
|
| 12 |
#' \dontrun{
|
|
| 13 |
#' # Validate from file path |
|
| 14 |
#' results <- validate_ideal_transcript_structure( |
|
| 15 |
#' file_path = "path/to/transcript.vtt" |
|
| 16 |
#' ) |
|
| 17 |
#' |
|
| 18 |
#' # Validate from data frame |
|
| 19 |
#' transcript_data <- load_zoom_transcript("path/to/transcript.vtt")
|
|
| 20 |
#' results <- validate_ideal_transcript_structure(transcript_data = transcript_data) |
|
| 21 |
#' } |
|
| 22 |
validate_ideal_transcript_structure <- function(transcript_data = NULL, |
|
| 23 |
file_path = NULL, |
|
| 24 |
strict_mode = TRUE) {
|
|
| 25 |
# DEPRECATED: This function will be removed in the next version |
|
| 26 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 27 | 25x |
warning("Function 'validate_ideal_transcript_structure' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 28 | ||
| 29 |
# Initialize results |
|
| 30 | 25x |
results <- list( |
| 31 | 25x |
status = "PENDING", |
| 32 | 25x |
issues = list(), |
| 33 | 25x |
warnings = list(), |
| 34 | 25x |
recommendations = list(), |
| 35 | 25x |
validation_details = list() |
| 36 |
) |
|
| 37 | ||
| 38 |
# Validate inputs |
|
| 39 | 25x |
if (is.null(transcript_data) && is.null(file_path)) {
|
| 40 | 1x |
stop("Either transcript_data or file_path must be provided")
|
| 41 |
} |
|
| 42 | ||
| 43 |
# Load data if file path provided |
|
| 44 | 24x |
if (!is.null(file_path)) {
|
| 45 | 2x |
if (!file.exists(file_path)) {
|
| 46 | 1x |
results$status <- "FAIL" |
| 47 | 1x |
results$issues$file_not_found <- "Transcript file not found" |
| 48 | 1x |
return(results) |
| 49 |
} |
|
| 50 | ||
| 51 | 1x |
tryCatch( |
| 52 |
{
|
|
| 53 | 1x |
transcript_data <- load_zoom_transcript(file_path) |
| 54 |
}, |
|
| 55 | 1x |
error = function(e) {
|
| 56 | ! |
results$status <- "FAIL" |
| 57 | ! |
results$issues$load_error <- paste("Failed to load transcript:", e$message)
|
| 58 |
} |
|
| 59 |
) |
|
| 60 |
} |
|
| 61 | ||
| 62 | 23x |
if (is.null(transcript_data) || nrow(transcript_data) == 0) {
|
| 63 | 1x |
results$status <- "FAIL" |
| 64 | 1x |
results$issues$empty_data <- "Transcript data is empty or invalid" |
| 65 | 1x |
return(results) |
| 66 |
} |
|
| 67 | ||
| 68 |
# Validate data structure |
|
| 69 | 21x |
structure_validation <- validate_transcript_structure(transcript_data, strict_mode) |
| 70 | 21x |
results$validation_details$structure <- structure_validation |
| 71 | ||
| 72 |
# Validate VTT format compliance |
|
| 73 | 21x |
vtt_validation <- validate_vtt_format(transcript_data, strict_mode) |
| 74 | 21x |
results$validation_details$vtt_format <- vtt_validation |
| 75 | ||
| 76 |
# Validate required fields |
|
| 77 | 21x |
fields_validation <- validate_required_fields(transcript_data, strict_mode) |
| 78 | 21x |
results$validation_details$required_fields <- fields_validation |
| 79 | ||
| 80 |
# Validate timestamp formatting |
|
| 81 | 21x |
timestamp_validation <- validate_timestamp_format(transcript_data, strict_mode) |
| 82 | 21x |
results$validation_details$timestamps <- timestamp_validation |
| 83 | ||
| 84 |
# Determine overall status |
|
| 85 | 21x |
all_issues <- c( |
| 86 | 21x |
results$issues, |
| 87 | 21x |
structure_validation$issues, |
| 88 | 21x |
vtt_validation$issues, |
| 89 | 21x |
fields_validation$issues, |
| 90 | 21x |
timestamp_validation$issues |
| 91 |
) |
|
| 92 | ||
| 93 | 21x |
all_warnings <- c( |
| 94 | 21x |
results$warnings, |
| 95 | 21x |
structure_validation$warnings, |
| 96 | 21x |
vtt_validation$warnings, |
| 97 | 21x |
fields_validation$warnings, |
| 98 | 21x |
timestamp_validation$warnings |
| 99 |
) |
|
| 100 | ||
| 101 | 21x |
results$issues <- all_issues |
| 102 | 21x |
results$warnings <- all_warnings |
| 103 | ||
| 104 |
# Set final status |
|
| 105 | 21x |
if (length(all_issues) > 0) {
|
| 106 | 4x |
results$status <- "FAIL" |
| 107 | 17x |
} else if (length(all_warnings) > 0) {
|
| 108 | 6x |
results$status <- "WARN" |
| 109 |
} else {
|
|
| 110 | 11x |
results$status <- "PASS" |
| 111 |
} |
|
| 112 | ||
| 113 |
# Generate recommendations |
|
| 114 | 21x |
results$recommendations <- generate_structure_recommendations(results) |
| 115 | ||
| 116 | 21x |
return(results) |
| 117 |
} |
|
| 118 | ||
| 119 |
#' Validate Ideal Course Transcript Content Quality |
|
| 120 |
#' |
|
| 121 |
#' Validates the content quality of ideal course transcripts to ensure they |
|
| 122 |
#' contain realistic dialogue patterns, appropriate content length, and |
|
| 123 |
#' consistent speaker names. |
|
| 124 |
#' |
|
| 125 |
#' @param transcript_data Data frame containing transcript data |
|
| 126 |
#' @param quality_threshold Numeric. Quality threshold (0-1). Default: 0.8 |
|
| 127 |
#' @param check_realism Logical. Whether to check for realistic content. Default: TRUE |
|
| 128 |
#' @return Validation results list with quality metrics and issues |
|
| 129 |
#' @export |
|
| 130 |
#' @examples |
|
| 131 |
#' \dontrun{
|
|
| 132 |
#' transcript_data <- load_zoom_transcript("path/to/transcript.vtt")
|
|
| 133 |
#' results <- validate_ideal_content_quality(transcript_data) |
|
| 134 |
#' } |
|
| 135 |
validate_ideal_content_quality <- function(transcript_data = NULL, |
|
| 136 |
quality_threshold = 0.8, |
|
| 137 |
check_realism = TRUE) {
|
|
| 138 |
# DEPRECATED: This function will be removed in the next version |
|
| 139 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 140 | 22x |
warning("Function 'validate_ideal_content_quality' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 141 | ||
| 142 |
# Initialize results |
|
| 143 | 22x |
results <- list( |
| 144 | 22x |
status = "PENDING", |
| 145 | 22x |
quality_score = 0, |
| 146 | 22x |
issues = list(), |
| 147 | 22x |
warnings = list(), |
| 148 | 22x |
quality_metrics = list(), |
| 149 | 22x |
validation_details = list(), |
| 150 | 22x |
recommendations = list() |
| 151 |
) |
|
| 152 | ||
| 153 |
# Validate input |
|
| 154 | 22x |
if (is.null(transcript_data)) {
|
| 155 | 1x |
stop("transcript_data cannot be NULL")
|
| 156 |
} |
|
| 157 | 21x |
if (nrow(transcript_data) == 0) {
|
| 158 | ! |
results$status <- "FAIL" |
| 159 | ! |
results$issues$empty_data <- "Transcript data is empty or invalid" |
| 160 | ! |
return(results) |
| 161 |
} |
|
| 162 | ||
| 163 |
# Calculate quality metrics |
|
| 164 | 20x |
quality_metrics <- calculate_content_quality_metrics(transcript_data) |
| 165 | 20x |
results$quality_metrics <- quality_metrics |
| 166 | ||
| 167 |
# Check dialogue length patterns |
|
| 168 | 20x |
dialogue_validation <- validate_dialogue_length(transcript_data) |
| 169 | 20x |
results$validation_details$dialogue_length <- dialogue_validation |
| 170 | ||
| 171 |
# Check speaker name consistency |
|
| 172 | 20x |
name_validation <- validate_speaker_consistency(transcript_data) |
| 173 | 20x |
results$validation_details$speaker_consistency <- name_validation |
| 174 | ||
| 175 |
# Check content diversity |
|
| 176 | 20x |
diversity_validation <- validate_content_diversity(transcript_data) |
| 177 | 20x |
results$validation_details$content_diversity <- diversity_validation |
| 178 | ||
| 179 |
# Check for realistic patterns if requested |
|
| 180 | 20x |
if (check_realism) {
|
| 181 | 19x |
realism_validation <- validate_realistic_patterns(transcript_data) |
| 182 | 19x |
results$validation_details$realism <- realism_validation |
| 183 |
} |
|
| 184 | ||
| 185 |
# Calculate overall quality score |
|
| 186 | 20x |
quality_score <- calculate_overall_quality_score(results$validation_details) |
| 187 | 20x |
results$quality_score <- quality_score |
| 188 | ||
| 189 |
# Determine status based on quality score and validation results |
|
| 190 | 20x |
results$status <- determine_validation_status(results$validation_details) |
| 191 | ||
| 192 |
# Collect all issues and warnings efficiently |
|
| 193 | 20x |
all_issues <- list() |
| 194 | 20x |
all_warnings <- list() |
| 195 | 20x |
for (detail in results$validation_details) {
|
| 196 | 79x |
all_issues <- c(all_issues, detail$issues) |
| 197 | 79x |
all_warnings <- c(all_warnings, detail$warnings) |
| 198 |
} |
|
| 199 | ||
| 200 | 20x |
results$issues <- all_issues |
| 201 | 20x |
results$warnings <- all_warnings |
| 202 | ||
| 203 |
# Generate recommendations |
|
| 204 | 20x |
results$recommendations <- generate_content_recommendations(results) |
| 205 | ||
| 206 | 20x |
return(results) |
| 207 |
} |
|
| 208 | ||
| 209 |
#' Validate Ideal Course Transcript Timing Consistency |
|
| 210 |
#' |
|
| 211 |
#' Validates the timing consistency of ideal course transcripts to ensure |
|
| 212 |
#' timestamps are logical, chronological, and without overlaps. |
|
| 213 |
#' |
|
| 214 |
#' @param transcript_data Data frame containing transcript data |
|
| 215 |
#' @param max_gap_seconds Numeric. Maximum allowed gap between entries. Default: 300 |
|
| 216 |
#' @param check_overlaps Logical. Whether to check for overlapping timestamps. Default: TRUE |
|
| 217 |
#' @return Validation results list with timing analysis and issues |
|
| 218 |
#' @export |
|
| 219 |
#' @examples |
|
| 220 |
#' \dontrun{
|
|
| 221 |
#' transcript_data <- load_zoom_transcript("path/to/transcript.vtt")
|
|
| 222 |
#' results <- validate_ideal_timing_consistency(transcript_data) |
|
| 223 |
#' } |
|
| 224 |
validate_ideal_timing_consistency <- function(transcript_data = NULL, |
|
| 225 |
max_gap_seconds = 300, |
|
| 226 |
check_overlaps = TRUE) {
|
|
| 227 |
# DEPRECATED: This function will be removed in the next version |
|
| 228 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 229 | 21x |
warning("Function 'validate_ideal_timing_consistency' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 230 | ||
| 231 |
# DEPRECATED: This function will be removed in the next version |
|
| 232 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 233 | 21x |
warning("Function 'validate_ideal_timing_consistency' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 234 | ||
| 235 |
# Initialize results |
|
| 236 | 21x |
results <- list( |
| 237 | 21x |
status = "PENDING", |
| 238 | 21x |
issues = list(), |
| 239 | 21x |
warnings = list(), |
| 240 | 21x |
validation_details = list(), |
| 241 | 21x |
timing_analysis = list(), |
| 242 | 21x |
recommendations = list() |
| 243 |
) |
|
| 244 | ||
| 245 |
# Validate input |
|
| 246 | 21x |
if (is.null(transcript_data)) {
|
| 247 | 1x |
stop("transcript_data cannot be NULL")
|
| 248 |
} |
|
| 249 | 20x |
if (nrow(transcript_data) == 0) {
|
| 250 | ! |
results$status <- "FAIL" |
| 251 | ! |
results$issues$empty_data <- "Transcript data is empty or invalid" |
| 252 | ! |
return(results) |
| 253 |
} |
|
| 254 | ||
| 255 |
# Check for required timing columns |
|
| 256 | 19x |
if (!all(c("start", "end") %in% names(transcript_data))) {
|
| 257 | ! |
results$status <- "FAIL" |
| 258 | ! |
results$issues$missing_timing_columns <- "Missing start/end timestamp columns" |
| 259 | ! |
return(results) |
| 260 |
} |
|
| 261 | ||
| 262 |
# Validate chronological order |
|
| 263 | 19x |
chronological_validation <- validate_chronological_order(transcript_data) |
| 264 | 19x |
results$validation_details$chronological <- chronological_validation |
| 265 | ||
| 266 |
# Check for overlapping timestamps |
|
| 267 | 19x |
if (check_overlaps) {
|
| 268 | 18x |
overlap_validation <- validate_no_overlaps(transcript_data) |
| 269 | 18x |
results$validation_details$overlaps <- overlap_validation |
| 270 |
} |
|
| 271 | ||
| 272 |
# Validate duration calculations |
|
| 273 | 19x |
duration_validation <- validate_duration_calculations(transcript_data) |
| 274 | 19x |
results$validation_details$duration <- duration_validation |
| 275 | ||
| 276 |
# Check for reasonable gaps |
|
| 277 | 19x |
gap_validation <- validate_reasonable_gaps(transcript_data, max_gap_seconds) |
| 278 | 19x |
results$validation_details$gaps <- gap_validation |
| 279 | ||
| 280 |
# Analyze timing patterns |
|
| 281 | 19x |
timing_analysis <- analyze_timing_patterns(transcript_data) |
| 282 | 19x |
results$timing_analysis <- timing_analysis |
| 283 | ||
| 284 |
# Determine overall status |
|
| 285 | 19x |
all_issues <- list() |
| 286 | 19x |
all_warnings <- list() |
| 287 | 19x |
for (detail in results$validation_details) {
|
| 288 | 75x |
all_issues <- c(all_issues, detail$issues) |
| 289 | 75x |
all_warnings <- c(all_warnings, detail$warnings) |
| 290 |
} |
|
| 291 | ||
| 292 | 19x |
results$issues <- all_issues |
| 293 | 19x |
results$warnings <- all_warnings |
| 294 | ||
| 295 | 19x |
results$status <- determine_validation_status(results$validation_details) |
| 296 | ||
| 297 |
# Generate recommendations |
|
| 298 | 19x |
results$recommendations <- generate_timing_recommendations(results) |
| 299 | ||
| 300 | 19x |
return(results) |
| 301 |
} |
|
| 302 | ||
| 303 |
#' Validate Ideal Course Transcript Name Coverage |
|
| 304 |
#' |
|
| 305 |
#' Validates that ideal course transcripts include comprehensive name coverage |
|
| 306 |
#' for all test scenarios, name variations, and edge cases. |
|
| 307 |
#' |
|
| 308 |
#' @param transcript_data Data frame containing transcript data |
|
| 309 |
#' @param expected_names Character vector. Expected speaker names. Default: NULL |
|
| 310 |
#' @param check_variations Logical. Whether to check for name variations. Default: TRUE |
|
| 311 |
#' @param check_edge_cases Logical. Whether to check for edge case names. Default: TRUE |
|
| 312 |
#' @return Validation results list with name coverage analysis |
|
| 313 |
#' # # # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 314 |
#' @examples |
|
| 315 |
#' \dontrun{
|
|
| 316 |
#' transcript_data <- load_zoom_transcript("path/to/transcript.vtt")
|
|
| 317 |
#' results <- validate_ideal_name_coverage(transcript_data) |
|
| 318 |
#' |
|
| 319 |
#' # With expected names |
|
| 320 |
#' expected_names <- c("Professor Ed", "Tom Miller", "Samantha Smith")
|
|
| 321 |
#' results <- validate_ideal_name_coverage(transcript_data, expected_names) |
|
| 322 |
#' } |
|
| 323 |
validate_ideal_name_coverage <- function(transcript_data = NULL, |
|
| 324 |
expected_names = NULL, |
|
| 325 |
check_variations = TRUE, |
|
| 326 |
check_edge_cases = TRUE) {
|
|
| 327 |
# DEPRECATED: This function will be removed in the next version |
|
| 328 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 329 | 22x |
warning("Function 'validate_ideal_name_coverage' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 330 | ||
| 331 |
# DEPRECATED: This function will be removed in the next version |
|
| 332 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 333 | 22x |
warning("Function 'validate_ideal_name_coverage' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 334 | ||
| 335 |
# Initialize results |
|
| 336 | 22x |
results <- list( |
| 337 | 22x |
status = "PENDING", |
| 338 | 22x |
issues = list(), |
| 339 | 22x |
warnings = list(), |
| 340 | 22x |
validation_details = list(), |
| 341 | 22x |
coverage_analysis = list(), |
| 342 | 22x |
recommendations = list() |
| 343 |
) |
|
| 344 | ||
| 345 |
# Validate input |
|
| 346 | 22x |
if (is.null(transcript_data)) {
|
| 347 | 1x |
stop("transcript_data cannot be NULL")
|
| 348 |
} |
|
| 349 | 21x |
if (nrow(transcript_data) == 0) {
|
| 350 | ! |
results$status <- "FAIL" |
| 351 | ! |
results$issues$empty_data <- "Transcript data is empty or invalid" |
| 352 | ! |
return(results) |
| 353 |
} |
|
| 354 | ||
| 355 |
# Check for name column |
|
| 356 | 20x |
if (!"name" %in% names(transcript_data)) {
|
| 357 | ! |
results$status <- "FAIL" |
| 358 | ! |
results$issues$missing_name_column <- "Missing name column" |
| 359 | ! |
return(results) |
| 360 |
} |
|
| 361 | ||
| 362 |
# Analyze name coverage |
|
| 363 | 20x |
coverage_analysis <- analyze_name_coverage(transcript_data) |
| 364 | 20x |
results$coverage_analysis <- coverage_analysis |
| 365 | ||
| 366 |
# Check expected names if provided |
|
| 367 | 20x |
if (!is.null(expected_names)) {
|
| 368 | 2x |
expected_validation <- validate_expected_names(transcript_data, expected_names) |
| 369 | 2x |
results$validation_details$expected_names <- expected_validation |
| 370 |
} |
|
| 371 | ||
| 372 |
# Check name variations |
|
| 373 | 20x |
if (check_variations) {
|
| 374 | 19x |
variation_validation <- validate_name_variations(transcript_data) |
| 375 | 19x |
results$validation_details$name_variations <- variation_validation |
| 376 |
} |
|
| 377 | ||
| 378 |
# Check edge cases |
|
| 379 | 20x |
if (check_edge_cases) {
|
| 380 | 19x |
edge_case_validation <- validate_name_edge_cases(transcript_data) |
| 381 | 19x |
results$validation_details$edge_cases <- edge_case_validation |
| 382 |
} |
|
| 383 | ||
| 384 |
# Check scenario completeness |
|
| 385 | 20x |
scenario_validation <- validate_scenario_completeness(transcript_data) |
| 386 | 20x |
results$validation_details$scenarios <- scenario_validation |
| 387 | ||
| 388 |
# Determine overall status |
|
| 389 | 20x |
all_issues <- list() |
| 390 | 20x |
all_warnings <- list() |
| 391 | ||
| 392 |
# Collect all issues and warnings efficiently |
|
| 393 | 20x |
for (detail in results$validation_details) {
|
| 394 | 60x |
all_issues <- c(all_issues, detail$issues) |
| 395 | 60x |
all_warnings <- c(all_warnings, detail$warnings) |
| 396 |
} |
|
| 397 | ||
| 398 | 20x |
results$issues <- all_issues |
| 399 | 20x |
results$warnings <- all_warnings |
| 400 | ||
| 401 | 20x |
results$status <- determine_validation_status(results$validation_details) |
| 402 | ||
| 403 |
# Generate recommendations |
|
| 404 | 20x |
results$recommendations <- generate_name_coverage_recommendations(results) |
| 405 | ||
| 406 | 20x |
return(results) |
| 407 |
} |
|
| 408 | ||
| 409 |
#' Comprehensive Validation for Ideal Course Transcripts |
|
| 410 |
#' |
|
| 411 |
#' Performs comprehensive validation of ideal course transcripts including |
|
| 412 |
#' structure, content quality, timing consistency, and name coverage. |
|
| 413 |
#' |
|
| 414 |
#' @param transcript_data Data frame containing transcript data (optional) |
|
| 415 |
#' @param file_path Path to transcript file (optional) |
|
| 416 |
#' @param validation_options List. Custom validation options. Default: NULL |
|
| 417 |
#' @param detailed_report Logical. Whether to generate detailed report. Default: TRUE |
|
| 418 |
#' @return Comprehensive validation results |
|
| 419 |
#' # # # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 420 |
#' @examples |
|
| 421 |
#' \dontrun{
|
|
| 422 |
#' # Validate from file |
|
| 423 |
#' results <- validate_ideal_transcript_comprehensive( |
|
| 424 |
#' file_path = "path/to/transcript.vtt" |
|
| 425 |
#' ) |
|
| 426 |
#' |
|
| 427 |
#' # Validate from data with custom options |
|
| 428 |
#' transcript_data <- load_zoom_transcript("path/to/transcript.vtt")
|
|
| 429 |
#' options <- list(quality_threshold = 0.9, strict_mode = FALSE) |
|
| 430 |
#' results <- validate_ideal_transcript_comprehensive( |
|
| 431 |
#' transcript_data = transcript_data, |
|
| 432 |
#' validation_options = options |
|
| 433 |
#' ) |
|
| 434 |
#' } |
|
| 435 |
validate_ideal_transcript_comprehensive <- function(transcript_data = NULL, |
|
| 436 |
file_path = NULL, |
|
| 437 |
validation_options = NULL, |
|
| 438 |
detailed_report = TRUE) {
|
|
| 439 |
# DEPRECATED: This function will be removed in the next version |
|
| 440 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 441 | 10x |
warning("Function 'validate_ideal_transcript_comprehensive' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 442 | ||
| 443 |
# DEPRECATED: This function will be removed in the next version |
|
| 444 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 445 | 10x |
warning("Function 'validate_ideal_transcript_comprehensive' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 446 | ||
| 447 |
# Set default options |
|
| 448 | 10x |
if (is.null(validation_options)) {
|
| 449 | 9x |
validation_options <- list( |
| 450 | 9x |
strict_mode = TRUE, |
| 451 | 9x |
quality_threshold = 0.8, |
| 452 | 9x |
check_realism = TRUE, |
| 453 | 9x |
max_gap_seconds = 300, |
| 454 | 9x |
check_overlaps = TRUE, |
| 455 | 9x |
check_variations = TRUE, |
| 456 | 9x |
check_edge_cases = TRUE |
| 457 |
) |
|
| 458 |
} |
|
| 459 | ||
| 460 |
# Initialize comprehensive results |
|
| 461 | 10x |
results <- list( |
| 462 | 10x |
timestamp = Sys.time(), |
| 463 | 10x |
overall_status = "PENDING", |
| 464 | 10x |
validation_results = list(), |
| 465 | 10x |
summary = list(), |
| 466 | 10x |
recommendations = list(), |
| 467 | 10x |
detailed_report = NULL |
| 468 |
) |
|
| 469 | ||
| 470 |
# Load data if file path provided |
|
| 471 | 10x |
if (!is.null(file_path)) {
|
| 472 | 2x |
if (!file.exists(file_path)) {
|
| 473 | 1x |
results$overall_status <- "FAIL" |
| 474 | 1x |
results$validation_results$file_error <- "File not found" |
| 475 | 1x |
return(results) |
| 476 |
} |
|
| 477 | ||
| 478 | 1x |
tryCatch( |
| 479 |
{
|
|
| 480 | 1x |
transcript_data <- load_zoom_transcript(file_path) |
| 481 |
}, |
|
| 482 | 1x |
error = function(e) {
|
| 483 | ! |
results$overall_status <- "FAIL" |
| 484 | ! |
results$validation_results$load_error <- paste("Failed to load transcript:", e$message)
|
| 485 |
} |
|
| 486 |
) |
|
| 487 |
} |
|
| 488 | ||
| 489 | 9x |
if (is.null(transcript_data) || nrow(transcript_data) == 0) {
|
| 490 | ! |
results$overall_status <- "FAIL" |
| 491 | ! |
results$validation_results$data_error <- "Transcript data is empty or invalid" |
| 492 | ! |
return(results) |
| 493 |
} |
|
| 494 | ||
| 495 |
# Run all validation checks |
|
| 496 | 9x |
results$validation_results$structure <- validate_ideal_transcript_structure( |
| 497 | 9x |
transcript_data = transcript_data, |
| 498 | 9x |
strict_mode = validation_options$strict_mode |
| 499 |
) |
|
| 500 | ||
| 501 | 9x |
results$validation_results$content_quality <- validate_ideal_content_quality( |
| 502 | 9x |
transcript_data = transcript_data, |
| 503 | 9x |
quality_threshold = validation_options$quality_threshold, |
| 504 | 9x |
check_realism = validation_options$check_realism |
| 505 |
) |
|
| 506 | ||
| 507 | 9x |
results$validation_results$timing_consistency <- validate_ideal_timing_consistency( |
| 508 | 9x |
transcript_data = transcript_data, |
| 509 | 9x |
max_gap_seconds = validation_options$max_gap_seconds, |
| 510 | 9x |
check_overlaps = validation_options$check_overlaps |
| 511 |
) |
|
| 512 | ||
| 513 | 9x |
results$validation_results$name_coverage <- validate_ideal_name_coverage( |
| 514 | 9x |
transcript_data = transcript_data, |
| 515 | 9x |
check_variations = validation_options$check_variations, |
| 516 | 9x |
check_edge_cases = validation_options$check_edge_cases |
| 517 |
) |
|
| 518 | ||
| 519 |
# Calculate overall status |
|
| 520 | 9x |
all_statuses <- sapply(results$validation_results, function(x) x$status) |
| 521 | ||
| 522 | 9x |
if (all(all_statuses == "PASS")) {
|
| 523 | 2x |
results$overall_status <- "PASS" |
| 524 | 7x |
} else if (any(all_statuses == "FAIL")) {
|
| 525 | ! |
results$overall_status <- "FAIL" |
| 526 |
} else {
|
|
| 527 | 7x |
results$overall_status <- "WARN" |
| 528 |
} |
|
| 529 | ||
| 530 |
# Generate summary |
|
| 531 | 9x |
results$summary <- generate_comprehensive_summary(results) |
| 532 | ||
| 533 |
# Generate recommendations |
|
| 534 | 9x |
results$recommendations <- generate_comprehensive_recommendations(results) |
| 535 | ||
| 536 |
# Generate detailed report if requested |
|
| 537 | 9x |
if (detailed_report) {
|
| 538 | 8x |
results$detailed_report <- generate_detailed_validation_report(results) |
| 539 |
} |
|
| 540 | ||
| 541 | 9x |
return(results) |
| 542 |
} |
|
| 543 | ||
| 544 |
# Helper functions for structure validation |
|
| 545 | ||
| 546 |
#' Validate transcript structure |
|
| 547 |
#' @keywords internal |
|
| 548 |
validate_transcript_structure <- function(transcript_data, strict_mode) {
|
|
| 549 |
# DEPRECATED: This function will be removed in the next version |
|
| 550 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 551 | 21x |
warning("Function 'validate_transcript_structure' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 552 | ||
| 553 | 21x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 554 | ||
| 555 |
# Check if it's a data frame |
|
| 556 | 21x |
if (!is.data.frame(transcript_data)) {
|
| 557 | ! |
results$status <- "FAIL" |
| 558 | ! |
results$issues$not_dataframe <- "Transcript data is not a data frame" |
| 559 | ! |
return(results) |
| 560 |
} |
|
| 561 | ||
| 562 |
# Check minimum required columns |
|
| 563 | 21x |
required_columns <- c("name", "comment", "start", "end")
|
| 564 | 21x |
missing_columns <- setdiff(required_columns, names(transcript_data)) |
| 565 | ||
| 566 | 21x |
if (length(missing_columns) > 0) {
|
| 567 | 2x |
results$status <- "FAIL" |
| 568 | 2x |
results$issues$missing_columns <- paste( |
| 569 | 2x |
"Missing required columns:", |
| 570 | 2x |
paste(missing_columns, collapse = ", ") |
| 571 |
) |
|
| 572 |
} |
|
| 573 | ||
| 574 |
# Check for empty data |
|
| 575 | 21x |
if (nrow(transcript_data) == 0) {
|
| 576 | ! |
results$status <- "FAIL" |
| 577 | ! |
results$issues$empty_data <- "Transcript data has no rows" |
| 578 |
} |
|
| 579 | ||
| 580 |
# Check for reasonable number of rows |
|
| 581 | 21x |
if (nrow(transcript_data) < 5) {
|
| 582 | 10x |
results$warnings$few_rows <- "Transcript has very few entries (< 5)" |
| 583 |
} |
|
| 584 | ||
| 585 | 21x |
return(results) |
| 586 |
} |
|
| 587 | ||
| 588 |
#' Validate VTT format compliance |
|
| 589 |
#' @keywords internal |
|
| 590 |
validate_vtt_format <- function(transcript_data, strict_mode) {
|
|
| 591 |
# DEPRECATED: This function will be removed in the next version |
|
| 592 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 593 | 21x |
warning("Function 'validate_vtt_format' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 594 | ||
| 595 | 21x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 596 | ||
| 597 |
# Check for WEBVTT-like structure |
|
| 598 |
# This is a basic check - actual VTT parsing is done by load_zoom_transcript |
|
| 599 | ||
| 600 |
# Check that timestamps are in expected format |
|
| 601 | 21x |
if ("start" %in% names(transcript_data) && "end" %in% names(transcript_data)) {
|
| 602 |
# Check if timestamps are numeric (seconds) or character (HH:MM:SS.mmm) |
|
| 603 | 19x |
start_times <- transcript_data$start |
| 604 | 19x |
end_times <- transcript_data$end |
| 605 | ||
| 606 |
# Basic timestamp validation |
|
| 607 | 19x |
if (is.numeric(start_times) && is.numeric(end_times)) {
|
| 608 |
# Check for reasonable time ranges |
|
| 609 | 11x |
if (any(start_times < 0) || any(end_times < 0)) {
|
| 610 | ! |
results$issues$negative_timestamps <- "Found negative timestamps" |
| 611 |
} |
|
| 612 | ||
| 613 | 11x |
if (any(end_times <= start_times)) {
|
| 614 | ! |
results$issues$invalid_duration <- "Found entries where end time <= start time" |
| 615 |
} |
|
| 616 |
} |
|
| 617 |
} |
|
| 618 | ||
| 619 | 21x |
return(results) |
| 620 |
} |
|
| 621 | ||
| 622 |
#' Validate required fields |
|
| 623 |
#' @keywords internal |
|
| 624 |
validate_required_fields <- function(transcript_data, strict_mode) {
|
|
| 625 |
# DEPRECATED: This function will be removed in the next version |
|
| 626 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 627 | 21x |
warning("Function 'validate_required_fields' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 628 | ||
| 629 | 21x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 630 | ||
| 631 |
# Check for non-empty names |
|
| 632 | 21x |
if ("name" %in% names(transcript_data)) {
|
| 633 | 20x |
empty_names <- sum(is.na(transcript_data$name) | transcript_data$name == "") |
| 634 | 20x |
if (empty_names > 0) {
|
| 635 | 2x |
results$issues$empty_names <- paste("Found", empty_names, "entries with empty names")
|
| 636 |
} |
|
| 637 |
} |
|
| 638 | ||
| 639 |
# Check for non-empty comments |
|
| 640 | 21x |
if ("comment" %in% names(transcript_data)) {
|
| 641 | 20x |
empty_comments <- sum(is.na(transcript_data$comment) | transcript_data$comment == "") |
| 642 | 20x |
if (empty_comments > 0) {
|
| 643 | 1x |
results$warnings$empty_comments <- paste("Found", empty_comments, "entries with empty comments")
|
| 644 |
} |
|
| 645 |
} |
|
| 646 | ||
| 647 | 21x |
return(results) |
| 648 |
} |
|
| 649 | ||
| 650 |
#' Validate timestamp format |
|
| 651 |
#' @keywords internal |
|
| 652 |
validate_timestamp_format <- function(transcript_data, strict_mode) {
|
|
| 653 |
# DEPRECATED: This function will be removed in the next version |
|
| 654 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 655 | 21x |
warning("Function 'validate_timestamp_format' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 656 | ||
| 657 | 21x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 658 | ||
| 659 | 21x |
if ("start" %in% names(transcript_data) && "end" %in% names(transcript_data)) {
|
| 660 | 19x |
start_times <- transcript_data$start |
| 661 | 19x |
end_times <- transcript_data$end |
| 662 | ||
| 663 |
# Check for NA timestamps |
|
| 664 | 19x |
na_starts <- sum(is.na(start_times)) |
| 665 | 19x |
na_ends <- sum(is.na(end_times)) |
| 666 | ||
| 667 | 19x |
if (na_starts > 0 || na_ends > 0) {
|
| 668 | ! |
results$issues$na_timestamps <- paste("Found", na_starts, "NA start times and", na_ends, "NA end times")
|
| 669 |
} |
|
| 670 | ||
| 671 |
# Check for reasonable time ranges |
|
| 672 | 19x |
if (is.numeric(start_times) && is.numeric(end_times)) {
|
| 673 | 11x |
max_duration <- max(end_times - start_times, na.rm = TRUE) |
| 674 | 11x |
if (max_duration > 3600) { # More than 1 hour
|
| 675 | ! |
results$warnings$long_duration <- paste("Found entry with duration > 1 hour:", max_duration, "seconds")
|
| 676 |
} |
|
| 677 |
} |
|
| 678 |
} |
|
| 679 | ||
| 680 | 21x |
return(results) |
| 681 |
} |
|
| 682 | ||
| 683 |
# Helper functions for validation status determination |
|
| 684 | ||
| 685 |
#' Determine validation status from issues and warnings |
|
| 686 |
#' @keywords internal |
|
| 687 |
determine_validation_status <- function(validation_details) {
|
|
| 688 |
# DEPRECATED: This function will be removed in the next version |
|
| 689 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 690 | 59x |
warning("Function 'determine_validation_status' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 691 | ||
| 692 | 59x |
has_issues <- any(sapply(validation_details, function(x) length(x$issues) > 0)) |
| 693 | 59x |
has_warnings <- any(sapply(validation_details, function(x) length(x$warnings) > 0)) |
| 694 | ||
| 695 | 59x |
if (has_issues) {
|
| 696 | 4x |
return("FAIL")
|
| 697 | 55x |
} else if (has_warnings) {
|
| 698 | 23x |
return("WARN")
|
| 699 |
} else {
|
|
| 700 | 32x |
return("PASS")
|
| 701 |
} |
|
| 702 |
} |
|
| 703 | ||
| 704 |
# Helper functions for content quality validation |
|
| 705 | ||
| 706 |
#' Calculate content quality metrics |
|
| 707 |
#' @keywords internal |
|
| 708 |
calculate_content_quality_metrics <- function(transcript_data) {
|
|
| 709 |
# DEPRECATED: This function will be removed in the next version |
|
| 710 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 711 | 20x |
warning("Function 'calculate_content_quality_metrics' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 712 | ||
| 713 | 20x |
metrics <- list() |
| 714 | ||
| 715 |
# Basic metrics |
|
| 716 | 20x |
metrics$total_entries <- nrow(transcript_data) |
| 717 | 20x |
metrics$unique_speakers <- length(unique(transcript_data$name)) |
| 718 | ||
| 719 |
# Comment length metrics |
|
| 720 | 20x |
if ("comment" %in% names(transcript_data)) {
|
| 721 | 20x |
comment_lengths <- nchar(transcript_data$comment) |
| 722 | 20x |
metrics$avg_comment_length <- mean(comment_lengths, na.rm = TRUE) |
| 723 | 20x |
metrics$min_comment_length <- min(comment_lengths, na.rm = TRUE) |
| 724 | 20x |
metrics$max_comment_length <- max(comment_lengths, na.rm = TRUE) |
| 725 |
} |
|
| 726 | ||
| 727 |
# Word count metrics |
|
| 728 | 20x |
if ("wordcount" %in% names(transcript_data)) {
|
| 729 | 7x |
metrics$avg_words_per_comment <- mean(transcript_data$wordcount, na.rm = TRUE) |
| 730 | 7x |
metrics$total_words <- sum(transcript_data$wordcount, na.rm = TRUE) |
| 731 |
} |
|
| 732 | ||
| 733 | 20x |
return(metrics) |
| 734 |
} |
|
| 735 | ||
| 736 |
#' Validate dialogue length |
|
| 737 |
#' @keywords internal |
|
| 738 |
validate_dialogue_length <- function(transcript_data) {
|
|
| 739 |
# DEPRECATED: This function will be removed in the next version |
|
| 740 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 741 | 20x |
warning("Function 'validate_dialogue_length' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 742 | ||
| 743 | 20x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 744 | ||
| 745 | 20x |
if ("comment" %in% names(transcript_data)) {
|
| 746 | 20x |
comment_lengths <- nchar(transcript_data$comment) |
| 747 | ||
| 748 |
# Check for very short comments |
|
| 749 | 20x |
very_short <- sum(comment_lengths <= 2, na.rm = TRUE) |
| 750 | 20x |
if (very_short > 0) {
|
| 751 | 2x |
results$warnings$very_short_comments <- paste("Found", very_short, "comments with <= 2 characters")
|
| 752 |
} |
|
| 753 | ||
| 754 |
# Check for very long comments |
|
| 755 | 20x |
very_long <- sum(comment_lengths > 500, na.rm = TRUE) |
| 756 | 20x |
if (very_long > 0) {
|
| 757 | 1x |
results$warnings$very_long_comments <- paste("Found", very_long, "comments with > 500 characters")
|
| 758 |
} |
|
| 759 |
} |
|
| 760 | ||
| 761 | 20x |
return(results) |
| 762 |
} |
|
| 763 | ||
| 764 |
#' Validate speaker consistency |
|
| 765 |
#' @keywords internal |
|
| 766 |
validate_speaker_consistency <- function(transcript_data) {
|
|
| 767 |
# DEPRECATED: This function will be removed in the next version |
|
| 768 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 769 | 20x |
warning("Function 'validate_speaker_consistency' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 770 | ||
| 771 | 20x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 772 | ||
| 773 | 20x |
if ("name" %in% names(transcript_data)) {
|
| 774 | 20x |
unique_names <- unique(transcript_data$name) |
| 775 | ||
| 776 |
# Check for consistent name formats |
|
| 777 |
# This is a basic check - more sophisticated name validation would be needed |
|
| 778 | 20x |
name_lengths <- nchar(unique_names) |
| 779 | ||
| 780 |
# Check for very short names |
|
| 781 | 20x |
very_short_names <- sum(name_lengths < 2, na.rm = TRUE) |
| 782 | 20x |
if (very_short_names > 0) {
|
| 783 | 1x |
results$warnings$very_short_names <- paste("Found", very_short_names, "names with < 2 characters")
|
| 784 |
} |
|
| 785 | ||
| 786 |
# Check for very long names |
|
| 787 | 20x |
very_long_names <- sum(name_lengths > 50, na.rm = TRUE) |
| 788 | 20x |
if (very_long_names > 0) {
|
| 789 | ! |
results$warnings$very_long_names <- paste("Found", very_long_names, "names with > 50 characters")
|
| 790 |
} |
|
| 791 |
} |
|
| 792 | ||
| 793 | 20x |
return(results) |
| 794 |
} |
|
| 795 | ||
| 796 |
#' Validate content diversity |
|
| 797 |
#' @keywords internal |
|
| 798 |
validate_content_diversity <- function(transcript_data) {
|
|
| 799 |
# DEPRECATED: This function will be removed in the next version |
|
| 800 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 801 | 20x |
warning("Function 'validate_content_diversity' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 802 | ||
| 803 | 20x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 804 | ||
| 805 | 20x |
if ("comment" %in% names(transcript_data)) {
|
| 806 | 20x |
unique_comments <- unique(transcript_data$comment) |
| 807 | 20x |
total_comments <- nrow(transcript_data) |
| 808 | ||
| 809 |
# Calculate diversity ratio |
|
| 810 | 20x |
if (total_comments > 0) {
|
| 811 | 20x |
diversity_ratio <- length(unique_comments) / total_comments |
| 812 | ||
| 813 | 20x |
if (diversity_ratio < 0.8) {
|
| 814 | 2x |
results$warnings$low_diversity <- paste( |
| 815 | 2x |
"Low content diversity:", |
| 816 | 2x |
round(diversity_ratio, 2), |
| 817 | 2x |
"(many duplicate comments)" |
| 818 |
) |
|
| 819 |
} |
|
| 820 |
} |
|
| 821 |
} |
|
| 822 | ||
| 823 | 20x |
return(results) |
| 824 |
} |
|
| 825 | ||
| 826 |
#' Validate realistic patterns |
|
| 827 |
#' @keywords internal |
|
| 828 |
validate_realistic_patterns <- function(transcript_data) {
|
|
| 829 |
# DEPRECATED: This function will be removed in the next version |
|
| 830 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 831 | 19x |
warning("Function 'validate_realistic_patterns' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 832 | ||
| 833 | 19x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 834 | ||
| 835 |
# Check for realistic academic dialogue patterns |
|
| 836 | 19x |
if ("comment" %in% names(transcript_data)) {
|
| 837 | 19x |
comments <- tolower(transcript_data$comment) |
| 838 | ||
| 839 |
# Check for common academic words |
|
| 840 | 19x |
academic_words <- c( |
| 841 | 19x |
"discuss", "question", "answer", "explain", "understand", |
| 842 | 19x |
"analysis", "research", "study", "learn", "teach" |
| 843 |
) |
|
| 844 | ||
| 845 | 19x |
academic_word_count <- sum(sapply(academic_words, function(word) {
|
| 846 | 190x |
sum(grepl(word, comments, fixed = TRUE)) |
| 847 |
})) |
|
| 848 | ||
| 849 | 19x |
if (academic_word_count == 0) {
|
| 850 | 13x |
results$warnings$no_academic_content <- "No academic dialogue patterns detected" |
| 851 |
} |
|
| 852 |
} |
|
| 853 | ||
| 854 | 19x |
return(results) |
| 855 |
} |
|
| 856 | ||
| 857 |
#' Calculate overall quality score |
|
| 858 |
#' @keywords internal |
|
| 859 |
calculate_overall_quality_score <- function(validation_details) {
|
|
| 860 |
# DEPRECATED: This function will be removed in the next version |
|
| 861 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 862 | 20x |
warning("Function 'calculate_overall_quality_score' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 863 | ||
| 864 |
# Simple scoring based on validation results |
|
| 865 | 20x |
total_checks <- length(validation_details) |
| 866 | 20x |
passed_checks <- sum(sapply(validation_details, function(x) {
|
| 867 | 79x |
if (is.list(x) && "status" %in% names(x)) {
|
| 868 | 79x |
x$status == "PASS" |
| 869 |
} else {
|
|
| 870 | ! |
FALSE |
| 871 |
} |
|
| 872 |
})) |
|
| 873 | ||
| 874 | 20x |
return(passed_checks / total_checks) |
| 875 |
} |
|
| 876 | ||
| 877 |
# Helper functions for timing validation |
|
| 878 | ||
| 879 |
#' Validate chronological order |
|
| 880 |
#' @keywords internal |
|
| 881 |
validate_chronological_order <- function(transcript_data) {
|
|
| 882 |
# DEPRECATED: This function will be removed in the next version |
|
| 883 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 884 | 19x |
warning("Function 'validate_chronological_order' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 885 | ||
| 886 | 19x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 887 | ||
| 888 | 19x |
if ("start" %in% names(transcript_data) && "end" %in% names(transcript_data)) {
|
| 889 | 19x |
start_times <- as.numeric(transcript_data$start) |
| 890 | 19x |
end_times <- as.numeric(transcript_data$end) |
| 891 | ||
| 892 |
# Check for chronological order |
|
| 893 | 19x |
if (length(start_times) > 1) {
|
| 894 | 19x |
time_diffs <- diff(start_times) |
| 895 | 19x |
non_chronological <- sum(time_diffs < 0, na.rm = TRUE) |
| 896 | ||
| 897 | 19x |
if (non_chronological > 0) {
|
| 898 | 1x |
results$status <- "FAIL" |
| 899 | 1x |
results$issues$non_chronological <- paste("Found", non_chronological, "non-chronological entries")
|
| 900 |
} |
|
| 901 |
} |
|
| 902 |
} |
|
| 903 | ||
| 904 | 19x |
return(results) |
| 905 |
} |
|
| 906 | ||
| 907 |
#' Validate no overlaps |
|
| 908 |
#' @keywords internal |
|
| 909 |
validate_no_overlaps <- function(transcript_data) {
|
|
| 910 |
# DEPRECATED: This function will be removed in the next version |
|
| 911 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 912 | 18x |
warning("Function 'validate_no_overlaps' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 913 | ||
| 914 | 18x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 915 | ||
| 916 | 18x |
if ("start" %in% names(transcript_data) && "end" %in% names(transcript_data)) {
|
| 917 | 18x |
start_times <- as.numeric(transcript_data$start) |
| 918 | 18x |
end_times <- as.numeric(transcript_data$end) |
| 919 | ||
| 920 |
# Check for overlaps (end time of one entry > start time of next entry) |
|
| 921 | 18x |
if (length(start_times) > 1) {
|
| 922 | 18x |
overlaps <- 0 |
| 923 | 18x |
for (i in 1:(length(start_times) - 1)) {
|
| 924 | 289x |
if (end_times[i] > start_times[i + 1]) {
|
| 925 | 4x |
overlaps <- overlaps + 1 |
| 926 |
} |
|
| 927 |
} |
|
| 928 | ||
| 929 | 18x |
if (overlaps > 0) {
|
| 930 | 2x |
results$status <- "FAIL" |
| 931 | 2x |
results$issues$overlaps <- paste("Found", overlaps, "overlapping timestamp entries")
|
| 932 |
} |
|
| 933 |
} |
|
| 934 |
} |
|
| 935 | ||
| 936 | 18x |
return(results) |
| 937 |
} |
|
| 938 | ||
| 939 |
#' Validate duration calculations |
|
| 940 |
#' @keywords internal |
|
| 941 |
validate_duration_calculations <- function(transcript_data) {
|
|
| 942 |
# DEPRECATED: This function will be removed in the next version |
|
| 943 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 944 | 19x |
warning("Function 'validate_duration_calculations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 945 | ||
| 946 | 19x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 947 | ||
| 948 | 19x |
if ("start" %in% names(transcript_data) && "end" %in% names(transcript_data)) {
|
| 949 | 19x |
start_times <- as.numeric(transcript_data$start) |
| 950 | 19x |
end_times <- as.numeric(transcript_data$end) |
| 951 | ||
| 952 |
# Calculate durations |
|
| 953 | 19x |
calculated_durations <- end_times - start_times |
| 954 | ||
| 955 |
# Check for negative durations |
|
| 956 | 19x |
negative_durations <- sum(calculated_durations < 0, na.rm = TRUE) |
| 957 | 19x |
if (negative_durations > 0) {
|
| 958 | 1x |
results$status <- "FAIL" |
| 959 | 1x |
results$issues$negative_durations <- paste("Found", negative_durations, "entries with negative duration")
|
| 960 |
} |
|
| 961 | ||
| 962 |
# Check for zero durations |
|
| 963 | 19x |
zero_durations <- sum(calculated_durations == 0, na.rm = TRUE) |
| 964 | 19x |
if (zero_durations > 0) {
|
| 965 | ! |
results$warnings$zero_durations <- paste("Found", zero_durations, "entries with zero duration")
|
| 966 |
} |
|
| 967 | ||
| 968 |
# Check for very long durations |
|
| 969 | 19x |
very_long_durations <- sum(calculated_durations > 300, na.rm = TRUE) # > 5 minutes |
| 970 | 19x |
if (very_long_durations > 0) {
|
| 971 | ! |
results$warnings$very_long_durations <- paste("Found", very_long_durations, "entries with duration > 5 minutes")
|
| 972 |
} |
|
| 973 |
} |
|
| 974 | ||
| 975 | 19x |
return(results) |
| 976 |
} |
|
| 977 | ||
| 978 |
#' Validate reasonable gaps |
|
| 979 |
#' @keywords internal |
|
| 980 |
validate_reasonable_gaps <- function(transcript_data, max_gap_seconds) {
|
|
| 981 |
# DEPRECATED: This function will be removed in the next version |
|
| 982 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 983 | 19x |
warning("Function 'validate_reasonable_gaps' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 984 | ||
| 985 | 19x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 986 | ||
| 987 | 19x |
if ("start" %in% names(transcript_data) && "end" %in% names(transcript_data)) {
|
| 988 | 19x |
start_times <- as.numeric(transcript_data$start) |
| 989 | 19x |
end_times <- as.numeric(transcript_data$end) |
| 990 | ||
| 991 |
# Calculate gaps between entries (only if more than one entry) |
|
| 992 | 19x |
if (length(start_times) > 1) {
|
| 993 | 19x |
gaps <- start_times[2:length(start_times)] - end_times[1:(length(end_times) - 1)] |
| 994 | ||
| 995 |
# Check for large gaps |
|
| 996 | 19x |
large_gaps <- sum(gaps > max_gap_seconds, na.rm = TRUE) |
| 997 | 19x |
if (large_gaps > 0) {
|
| 998 | 1x |
results$warnings$large_gaps <- paste("Found", large_gaps, "gaps >", max_gap_seconds, "seconds")
|
| 999 |
} |
|
| 1000 |
} |
|
| 1001 |
} |
|
| 1002 | ||
| 1003 | 19x |
return(results) |
| 1004 |
} |
|
| 1005 | ||
| 1006 |
#' Analyze timing patterns |
|
| 1007 |
#' @keywords internal |
|
| 1008 |
analyze_timing_patterns <- function(transcript_data) {
|
|
| 1009 |
# DEPRECATED: This function will be removed in the next version |
|
| 1010 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1011 | 19x |
warning("Function 'analyze_timing_patterns' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1012 | ||
| 1013 | 19x |
analysis <- list() |
| 1014 | ||
| 1015 | 19x |
if ("start" %in% names(transcript_data) && "end" %in% names(transcript_data)) {
|
| 1016 | 19x |
start_times <- as.numeric(transcript_data$start) |
| 1017 | 19x |
end_times <- as.numeric(transcript_data$end) |
| 1018 | ||
| 1019 | 19x |
analysis$total_duration <- max(end_times, na.rm = TRUE) - min(start_times, na.rm = TRUE) |
| 1020 | 19x |
analysis$avg_entry_duration <- mean(end_times - start_times, na.rm = TRUE) |
| 1021 | 19x |
analysis$total_entries <- length(start_times) |
| 1022 | 19x |
analysis$entries_per_minute <- analysis$total_entries / (analysis$total_duration / 60) |
| 1023 |
} |
|
| 1024 | ||
| 1025 | 19x |
return(analysis) |
| 1026 |
} |
|
| 1027 | ||
| 1028 |
# Helper functions for name coverage validation |
|
| 1029 | ||
| 1030 |
#' Analyze name coverage |
|
| 1031 |
#' @keywords internal |
|
| 1032 |
analyze_name_coverage <- function(transcript_data) {
|
|
| 1033 |
# DEPRECATED: This function will be removed in the next version |
|
| 1034 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1035 | 20x |
warning("Function 'analyze_name_coverage' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1036 | ||
| 1037 | 20x |
analysis <- list() |
| 1038 | ||
| 1039 | 20x |
if ("name" %in% names(transcript_data)) {
|
| 1040 | 20x |
unique_names <- unique(transcript_data$name) |
| 1041 | ||
| 1042 | 20x |
analysis$total_unique_names <- length(unique_names) |
| 1043 | 20x |
analysis$unique_names <- unique_names |
| 1044 | 20x |
analysis$name_frequency <- table(transcript_data$name) |
| 1045 | ||
| 1046 |
# Analyze name patterns |
|
| 1047 | 20x |
analysis$name_lengths <- nchar(unique_names) |
| 1048 | 20x |
analysis$avg_name_length <- mean(analysis$name_lengths, na.rm = TRUE) |
| 1049 |
} |
|
| 1050 | ||
| 1051 | 20x |
return(analysis) |
| 1052 |
} |
|
| 1053 | ||
| 1054 |
#' Validate expected names |
|
| 1055 |
#' @keywords internal |
|
| 1056 |
validate_expected_names <- function(transcript_data, expected_names) {
|
|
| 1057 |
# DEPRECATED: This function will be removed in the next version |
|
| 1058 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1059 | 2x |
warning("Function 'validate_expected_names' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1060 | ||
| 1061 | 2x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 1062 | ||
| 1063 | 2x |
if ("name" %in% names(transcript_data)) {
|
| 1064 | 2x |
actual_names <- unique(transcript_data$name) |
| 1065 | 2x |
missing_names <- setdiff(expected_names, actual_names) |
| 1066 | 2x |
unexpected_names <- setdiff(actual_names, expected_names) |
| 1067 | ||
| 1068 | 2x |
if (length(missing_names) > 0) {
|
| 1069 | 1x |
results$status <- "FAIL" |
| 1070 | 1x |
results$issues$missing_expected_names <- paste( |
| 1071 | 1x |
"Missing expected names:", |
| 1072 | 1x |
paste(missing_names, collapse = ", ") |
| 1073 |
) |
|
| 1074 |
} |
|
| 1075 | ||
| 1076 | 2x |
if (length(unexpected_names) > 0) {
|
| 1077 | 1x |
results$warnings$unexpected_names <- paste( |
| 1078 | 1x |
"Found unexpected names:", |
| 1079 | 1x |
paste(unexpected_names, collapse = ", ") |
| 1080 |
) |
|
| 1081 |
} |
|
| 1082 |
} |
|
| 1083 | ||
| 1084 | 2x |
return(results) |
| 1085 |
} |
|
| 1086 | ||
| 1087 |
#' Validate name variations |
|
| 1088 |
#' @keywords internal |
|
| 1089 |
validate_name_variations <- function(transcript_data) {
|
|
| 1090 |
# DEPRECATED: This function will be removed in the next version |
|
| 1091 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1092 | 19x |
warning("Function 'validate_name_variations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1093 | ||
| 1094 | 19x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 1095 | ||
| 1096 | 19x |
if ("name" %in% names(transcript_data)) {
|
| 1097 | 19x |
unique_names <- unique(transcript_data$name) |
| 1098 | ||
| 1099 |
# Check for common name variation patterns |
|
| 1100 |
# This is a basic check - more sophisticated name matching would be needed |
|
| 1101 | ||
| 1102 |
# Check for formal vs informal variations |
|
| 1103 | 19x |
formal_patterns <- grepl("^(Dr\\.|Professor|Prof\\.)", unique_names)
|
| 1104 | 19x |
informal_patterns <- grepl("^(Tom|Sam|Rob|Tony|Ed)", unique_names)
|
| 1105 | ||
| 1106 | 19x |
if (sum(formal_patterns) == 0 && sum(informal_patterns) > 0) {
|
| 1107 | 1x |
results$warnings$no_name_variations <- "Only informal name variations detected (no formal variations)" |
| 1108 | 18x |
} else if (sum(formal_patterns) == 0 && sum(informal_patterns) == 0) {
|
| 1109 | ! |
results$warnings$no_name_variations <- "No name variations detected (formal/informal)" |
| 1110 |
} |
|
| 1111 |
} |
|
| 1112 | ||
| 1113 | 19x |
return(results) |
| 1114 |
} |
|
| 1115 | ||
| 1116 |
#' Validate name edge cases |
|
| 1117 |
#' @keywords internal |
|
| 1118 |
validate_name_edge_cases <- function(transcript_data) {
|
|
| 1119 |
# DEPRECATED: This function will be removed in the next version |
|
| 1120 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1121 | 19x |
warning("Function 'validate_name_edge_cases' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1122 | ||
| 1123 | 19x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 1124 | ||
| 1125 | 19x |
if ("name" %in% names(transcript_data)) {
|
| 1126 | 19x |
unique_names <- unique(transcript_data$name) |
| 1127 | ||
| 1128 |
# Check for edge case patterns |
|
| 1129 | 19x |
edge_cases <- list() |
| 1130 | ||
| 1131 |
# Names with special characters |
|
| 1132 | 19x |
special_char_names <- grepl("[^A-Za-z0-9 ]", unique_names)
|
| 1133 | 19x |
if (sum(special_char_names) > 0) {
|
| 1134 | 10x |
edge_cases$special_characters <- sum(special_char_names) |
| 1135 |
} |
|
| 1136 | ||
| 1137 |
# Names with numbers |
|
| 1138 | 19x |
number_names <- grepl("[0-9]", unique_names)
|
| 1139 | 19x |
if (sum(number_names) > 0) {
|
| 1140 | ! |
edge_cases$numbers <- sum(number_names) |
| 1141 |
} |
|
| 1142 | ||
| 1143 |
# Very long names |
|
| 1144 | 19x |
long_names <- nchar(unique_names) > 30 |
| 1145 | 19x |
if (sum(long_names) > 0) {
|
| 1146 | ! |
edge_cases$very_long <- sum(long_names) |
| 1147 |
} |
|
| 1148 | ||
| 1149 | 19x |
if (length(edge_cases) == 0) {
|
| 1150 | 9x |
results$warnings$no_edge_cases <- "No edge case names detected" |
| 1151 |
} else {
|
|
| 1152 | 10x |
results$validation_details$edge_cases_found <- edge_cases |
| 1153 |
} |
|
| 1154 |
} |
|
| 1155 | ||
| 1156 | 19x |
return(results) |
| 1157 |
} |
|
| 1158 | ||
| 1159 |
#' Validate scenario completeness |
|
| 1160 |
#' @keywords internal |
|
| 1161 |
validate_scenario_completeness <- function(transcript_data) {
|
|
| 1162 |
# DEPRECATED: This function will be removed in the next version |
|
| 1163 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1164 | 20x |
warning("Function 'validate_scenario_completeness' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1165 | ||
| 1166 | 20x |
results <- list(status = "PASS", issues = list(), warnings = list()) |
| 1167 | ||
| 1168 | 20x |
if ("name" %in% names(transcript_data)) {
|
| 1169 | 20x |
unique_names <- unique(transcript_data$name) |
| 1170 | ||
| 1171 |
# Check for common ideal course scenarios |
|
| 1172 | 20x |
scenarios <- list() |
| 1173 | ||
| 1174 |
# Instructor scenario |
|
| 1175 | 20x |
instructor_patterns <- grepl("^(Professor|Prof\\.|Dr\\.)", unique_names)
|
| 1176 | 20x |
scenarios$instructor <- sum(instructor_patterns) |
| 1177 | ||
| 1178 |
# Student scenarios |
|
| 1179 | 20x |
student_patterns <- grepl("^(Tom|Samantha|Robert|Wei|Jose|A\\.J\\.)", unique_names)
|
| 1180 | 20x |
scenarios$students <- sum(student_patterns) |
| 1181 | ||
| 1182 |
# Guest scenarios |
|
| 1183 | 20x |
guest_patterns <- grepl("^(Guest|Dr\\. Brown)", unique_names)
|
| 1184 | 20x |
scenarios$guests <- sum(guest_patterns) |
| 1185 | ||
| 1186 |
# Check for minimum scenario coverage |
|
| 1187 | 20x |
if (scenarios$instructor == 0) {
|
| 1188 | 1x |
results$warnings$no_instructor <- "No instructor names detected" |
| 1189 |
} |
|
| 1190 | ||
| 1191 | 20x |
if (scenarios$students == 0) {
|
| 1192 | ! |
results$warnings$no_students <- "No student names detected" |
| 1193 |
} |
|
| 1194 | ||
| 1195 | 20x |
results$validation_details$scenarios_found <- scenarios |
| 1196 |
} |
|
| 1197 | ||
| 1198 | 20x |
return(results) |
| 1199 |
} |
|
| 1200 | ||
| 1201 |
# Recommendation generation functions |
|
| 1202 | ||
| 1203 |
#' Generate structure recommendations |
|
| 1204 |
#' @keywords internal |
|
| 1205 |
generate_structure_recommendations <- function(results) {
|
|
| 1206 |
# DEPRECATED: This function will be removed in the next version |
|
| 1207 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1208 | 21x |
warning("Function 'generate_structure_recommendations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1209 | ||
| 1210 | 21x |
recommendations <- list() |
| 1211 | ||
| 1212 | 21x |
if (results$status == "FAIL") {
|
| 1213 | 4x |
recommendations$priority <- "Fix critical structure issues" |
| 1214 | ||
| 1215 | 4x |
if ("missing_columns" %in% names(results$issues)) {
|
| 1216 | 2x |
recommendations$missing_columns <- "Add required columns: name, comment, start, end" |
| 1217 |
} |
|
| 1218 | ||
| 1219 | 4x |
if ("empty_data" %in% names(results$issues)) {
|
| 1220 | ! |
recommendations$empty_data <- "Ensure transcript contains valid data" |
| 1221 |
} |
|
| 1222 |
} |
|
| 1223 | ||
| 1224 | 21x |
if (results$status == "WARN") {
|
| 1225 | 6x |
recommendations$priority <- "Address structure warnings" |
| 1226 | ||
| 1227 | 6x |
if ("few_rows" %in% names(results$warnings)) {
|
| 1228 | 6x |
recommendations$few_rows <- "Consider adding more transcript entries" |
| 1229 |
} |
|
| 1230 |
} |
|
| 1231 | ||
| 1232 | 21x |
return(recommendations) |
| 1233 |
} |
|
| 1234 | ||
| 1235 |
#' Generate content recommendations |
|
| 1236 |
#' @keywords internal |
|
| 1237 |
generate_content_recommendations <- function(results) {
|
|
| 1238 |
# DEPRECATED: This function will be removed in the next version |
|
| 1239 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1240 | 20x |
warning("Function 'generate_content_recommendations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1241 | ||
| 1242 | 20x |
recommendations <- list() |
| 1243 | ||
| 1244 | 20x |
if (results$status == "FAIL") {
|
| 1245 | ! |
recommendations$priority <- "Improve content quality" |
| 1246 |
} |
|
| 1247 | ||
| 1248 | 20x |
if (results$status == "WARN") {
|
| 1249 | 13x |
recommendations$priority <- "Address content quality warnings" |
| 1250 | ||
| 1251 | 13x |
if ("very_short_comments" %in% names(results$warnings)) {
|
| 1252 | 2x |
recommendations$short_comments <- "Consider expanding very short comments" |
| 1253 |
} |
|
| 1254 | ||
| 1255 | 13x |
if ("very_long_comments" %in% names(results$warnings)) {
|
| 1256 | 1x |
recommendations$long_comments <- "Consider breaking up very long comments" |
| 1257 |
} |
|
| 1258 | ||
| 1259 | 13x |
if ("low_diversity" %in% names(results$warnings)) {
|
| 1260 | 2x |
recommendations$diversity <- "Consider adding more diverse content" |
| 1261 |
} |
|
| 1262 |
} |
|
| 1263 | ||
| 1264 |
# Always provide at least one recommendation |
|
| 1265 | 20x |
if (length(recommendations) == 0) {
|
| 1266 | 7x |
recommendations$general <- "Content quality is good" |
| 1267 |
} |
|
| 1268 | ||
| 1269 | 20x |
return(recommendations) |
| 1270 |
} |
|
| 1271 | ||
| 1272 |
#' Generate timing recommendations |
|
| 1273 |
#' @keywords internal |
|
| 1274 |
generate_timing_recommendations <- function(results) {
|
|
| 1275 |
# DEPRECATED: This function will be removed in the next version |
|
| 1276 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1277 | 19x |
warning("Function 'generate_timing_recommendations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1278 | ||
| 1279 | 19x |
recommendations <- list() |
| 1280 | ||
| 1281 | 19x |
if (results$status == "FAIL") {
|
| 1282 | 3x |
recommendations$priority <- "Fix timing issues" |
| 1283 | ||
| 1284 | 3x |
if ("non_chronological" %in% names(results$issues)) {
|
| 1285 | 1x |
recommendations$chronological <- "Ensure timestamps are in chronological order" |
| 1286 |
} |
|
| 1287 | ||
| 1288 | 3x |
if ("overlaps" %in% names(results$issues)) {
|
| 1289 | 2x |
recommendations$overlaps <- "Remove overlapping timestamp entries" |
| 1290 |
} |
|
| 1291 |
} |
|
| 1292 | ||
| 1293 | 19x |
if (results$status == "WARN") {
|
| 1294 | 1x |
recommendations$priority <- "Address timing warnings" |
| 1295 | ||
| 1296 | 1x |
if ("large_gaps" %in% names(results$warnings)) {
|
| 1297 | 1x |
recommendations$gaps <- "Consider adding content to fill large time gaps" |
| 1298 |
} |
|
| 1299 |
} |
|
| 1300 | ||
| 1301 | 19x |
if (results$status == "PASS") {
|
| 1302 | 15x |
recommendations$general <- "Timing validation passed successfully" |
| 1303 |
} |
|
| 1304 | ||
| 1305 |
# Always return at least one recommendation |
|
| 1306 | 19x |
if (length(recommendations) == 0) {
|
| 1307 | ! |
recommendations$general <- "Review timing validation results" |
| 1308 |
} |
|
| 1309 | ||
| 1310 | 19x |
return(recommendations) |
| 1311 |
} |
|
| 1312 | ||
| 1313 |
#' Generate name coverage recommendations |
|
| 1314 |
#' @keywords internal |
|
| 1315 |
generate_name_coverage_recommendations <- function(results) {
|
|
| 1316 |
# DEPRECATED: This function will be removed in the next version |
|
| 1317 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1318 | 20x |
warning("Function 'generate_name_coverage_recommendations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1319 | ||
| 1320 | 20x |
recommendations <- list() |
| 1321 | ||
| 1322 | 20x |
if (results$status == "FAIL") {
|
| 1323 | 1x |
recommendations$priority <- "Improve name coverage" |
| 1324 | ||
| 1325 | 1x |
if ("missing_expected_names" %in% names(results$issues)) {
|
| 1326 | 1x |
recommendations$missing_names <- "Add missing expected speaker names" |
| 1327 |
} |
|
| 1328 |
} |
|
| 1329 | ||
| 1330 | 20x |
if (results$status == "WARN") {
|
| 1331 | 9x |
recommendations$priority <- "Enhance name coverage" |
| 1332 | ||
| 1333 | 9x |
if ("no_name_variations" %in% names(results$warnings)) {
|
| 1334 | 1x |
recommendations$variations <- "Add name variations (formal/informal)" |
| 1335 |
} |
|
| 1336 | ||
| 1337 | 9x |
if ("no_edge_cases" %in% names(results$warnings)) {
|
| 1338 | 9x |
recommendations$edge_cases <- "Add edge case names for comprehensive testing" |
| 1339 |
} |
|
| 1340 |
} |
|
| 1341 | ||
| 1342 | 20x |
if (results$status == "PASS") {
|
| 1343 | 10x |
recommendations$general <- "Name coverage validation passed successfully" |
| 1344 |
} |
|
| 1345 | ||
| 1346 |
# Always return at least one recommendation |
|
| 1347 | 20x |
if (length(recommendations) == 0) {
|
| 1348 | ! |
recommendations$general <- "Review name coverage validation results" |
| 1349 |
} |
|
| 1350 | ||
| 1351 | 20x |
return(recommendations) |
| 1352 |
} |
|
| 1353 | ||
| 1354 |
#' Generate comprehensive summary |
|
| 1355 |
#' @keywords internal |
|
| 1356 |
generate_comprehensive_summary <- function(results) {
|
|
| 1357 |
# DEPRECATED: This function will be removed in the next version |
|
| 1358 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1359 | 9x |
warning("Function 'generate_comprehensive_summary' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1360 | ||
| 1361 | 9x |
summary <- list() |
| 1362 | ||
| 1363 |
# Overall status |
|
| 1364 | 9x |
summary$overall_status <- results$overall_status |
| 1365 | ||
| 1366 |
# Individual validation statuses |
|
| 1367 | 9x |
summary$validation_statuses <- sapply(results$validation_results, function(x) x$status) |
| 1368 | ||
| 1369 |
# Count issues and warnings |
|
| 1370 | 9x |
total_issues <- sum(sapply(results$validation_results, function(x) length(x$issues))) |
| 1371 | 9x |
total_warnings <- sum(sapply(results$validation_results, function(x) length(x$warnings))) |
| 1372 | ||
| 1373 | 9x |
summary$total_issues <- total_issues |
| 1374 | 9x |
summary$total_warnings <- total_warnings |
| 1375 | ||
| 1376 |
# Quality score if available |
|
| 1377 | 9x |
if ("content_quality" %in% names(results$validation_results)) {
|
| 1378 | 9x |
summary$quality_score <- results$validation_results$content_quality$quality_score |
| 1379 |
} |
|
| 1380 | ||
| 1381 | 9x |
return(summary) |
| 1382 |
} |
|
| 1383 | ||
| 1384 |
#' Generate comprehensive recommendations |
|
| 1385 |
#' @keywords internal |
|
| 1386 |
generate_comprehensive_recommendations <- function(results) {
|
|
| 1387 |
# DEPRECATED: This function will be removed in the next version |
|
| 1388 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1389 | 9x |
warning("Function 'generate_comprehensive_recommendations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1390 | ||
| 1391 | 9x |
recommendations <- list() |
| 1392 | ||
| 1393 | 9x |
if (results$overall_status == "FAIL") {
|
| 1394 | ! |
recommendations$priority <- "Critical: Fix validation failures" |
| 1395 | ||
| 1396 |
# Collect all critical issues |
|
| 1397 | ! |
critical_issues <- unlist(lapply(results$validation_results, function(x) x$issues)) |
| 1398 | ! |
recommendations$critical_issues <- critical_issues |
| 1399 |
} |
|
| 1400 | ||
| 1401 | 9x |
if (results$overall_status == "WARN") {
|
| 1402 | 7x |
recommendations$priority <- "Address validation warnings" |
| 1403 | ||
| 1404 |
# Collect all warnings |
|
| 1405 | 7x |
all_warnings <- unlist(lapply(results$validation_results, function(x) x$warnings)) |
| 1406 | 7x |
recommendations$warnings <- all_warnings |
| 1407 |
} |
|
| 1408 | ||
| 1409 | 9x |
if (results$overall_status == "PASS") {
|
| 1410 | 2x |
recommendations$priority <- "Excellent: All validations passed" |
| 1411 | 2x |
recommendations$general <- "Transcript validation is complete and successful" |
| 1412 |
} |
|
| 1413 | ||
| 1414 |
# Always return at least one recommendation |
|
| 1415 | 9x |
if (length(recommendations) == 0) {
|
| 1416 | ! |
recommendations$general <- "Review validation results and address any issues" |
| 1417 |
} |
|
| 1418 | ||
| 1419 | 9x |
if (results$overall_status == "PASS") {
|
| 1420 | 2x |
recommendations$next_steps <- "Transcript is ready for use" |
| 1421 |
} |
|
| 1422 | ||
| 1423 | 9x |
return(recommendations) |
| 1424 |
} |
|
| 1425 | ||
| 1426 |
#' Generate detailed validation report |
|
| 1427 |
#' @keywords internal |
|
| 1428 |
generate_detailed_validation_report <- function(results) {
|
|
| 1429 |
# DEPRECATED: This function will be removed in the next version |
|
| 1430 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 1431 | 17x |
warning("Function 'generate_detailed_validation_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 1432 | ||
| 1433 | 17x |
report <- list() |
| 1434 | ||
| 1435 |
# Executive summary |
|
| 1436 | 17x |
report$executive_summary <- paste( |
| 1437 | 17x |
"Comprehensive validation completed at", results$timestamp, |
| 1438 | 17x |
"with overall status:", results$overall_status |
| 1439 |
) |
|
| 1440 | ||
| 1441 |
# Detailed results for each validation type |
|
| 1442 | 17x |
report$validation_details <- results$validation_results |
| 1443 | ||
| 1444 |
# Summary statistics |
|
| 1445 | 17x |
report$summary <- results$summary |
| 1446 | ||
| 1447 |
# Recommendations |
|
| 1448 | 17x |
report$recommendations <- results$recommendations |
| 1449 | ||
| 1450 | 17x |
return(report) |
| 1451 |
} |
| 1 |
#' Performance Benchmarking for Ideal Course Transcripts |
|
| 2 |
#' |
|
| 3 |
#' This function benchmarks the performance of ideal course transcript processing |
|
| 4 |
#' functions, measuring processing time, memory usage, and identifying bottlenecks. |
|
| 5 |
#' |
|
| 6 |
#' @param iterations Number of iterations for each benchmark (default: 5) |
|
| 7 |
#' @param output_file File to save benchmark results (default: "benchmark_results.rds") |
|
| 8 |
#' @param include_memory Whether to include memory profiling (default: TRUE) |
|
| 9 |
#' @param include_profiling Whether to include detailed profiling (default: FALSE) |
|
| 10 |
#' |
|
| 11 |
#' @return Benchmark results as a list |
|
| 12 |
#' @importFrom utils sessionInfo |
|
| 13 |
#' @noRd |
|
| 14 |
benchmark_ideal_transcripts <- function(iterations = 5, |
|
| 15 |
output_file = "benchmark_results.rds", |
|
| 16 |
include_memory = TRUE, |
|
| 17 |
include_profiling = FALSE) {
|
|
| 18 |
# DEPRECATED: This function will be removed in the next version |
|
| 19 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 20 | ! |
warning("Function 'benchmark_ideal_transcripts' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 21 | ||
| 22 |
# Check if microbenchmark is available |
|
| 23 | ! |
if (!requireNamespace("microbenchmark", quietly = TRUE)) {
|
| 24 | ! |
stop("microbenchmark package is required for benchmarking")
|
| 25 |
} |
|
| 26 | ||
| 27 |
# Check if pryr is available for memory profiling |
|
| 28 | ! |
if (include_memory) {
|
| 29 | ! |
if (!requireNamespace("pryr", quietly = TRUE)) {
|
| 30 | ! |
warning("pryr package not available, memory profiling disabled")
|
| 31 | ! |
include_memory <- FALSE |
| 32 |
} |
|
| 33 |
} |
|
| 34 | ||
| 35 |
# Initialize results storage |
|
| 36 | ! |
benchmark_results <- list( |
| 37 | ! |
timestamp = Sys.time(), |
| 38 | ! |
environment = utils::sessionInfo(), |
| 39 | ! |
benchmarks = list() |
| 40 |
) |
|
| 41 | ||
| 42 |
# Get ideal course transcript paths |
|
| 43 | ! |
transcript_dir <- system.file("extdata", "test_transcripts", package = "zoomstudentengagement")
|
| 44 | ! |
session_files <- c( |
| 45 | ! |
"ideal_course_session1.vtt", |
| 46 | ! |
"ideal_course_session2.vtt", |
| 47 | ! |
"ideal_course_session3.vtt" |
| 48 |
) |
|
| 49 | ||
| 50 |
# Validate that transcript files exist |
|
| 51 | ! |
missing_files <- session_files[!file.exists(file.path(transcript_dir, session_files))] |
| 52 | ! |
if (length(missing_files) > 0) {
|
| 53 | ! |
warning("Missing transcript files: ", paste(missing_files, collapse = ", "))
|
| 54 |
} |
|
| 55 | ||
| 56 |
# Benchmark 1: Individual transcript processing |
|
| 57 | ! |
individual_benchmarks <- list() |
| 58 | ||
| 59 | ! |
for (session_file in session_files) {
|
| 60 | ! |
session_path <- file.path(transcript_dir, session_file) |
| 61 | ! |
if (file.exists(session_path)) {
|
| 62 |
# Measure processing time |
|
| 63 | ! |
time_result <- microbenchmark::microbenchmark( |
| 64 | ! |
process_zoom_transcript(session_path), |
| 65 | ! |
times = iterations |
| 66 |
) |
|
| 67 | ||
| 68 |
# Measure memory usage if requested |
|
| 69 | ! |
memory_result <- NULL |
| 70 | ! |
if (include_memory) {
|
| 71 | ! |
memory_result <- measure_memory_usage( |
| 72 | ! |
process_zoom_transcript(session_path) |
| 73 |
) |
|
| 74 |
} |
|
| 75 | ||
| 76 | ! |
individual_benchmarks[[session_file]] <- list( |
| 77 | ! |
processing_time = time_result, |
| 78 | ! |
memory_usage = memory_result, |
| 79 | ! |
file_size = file.size(session_path) |
| 80 |
) |
|
| 81 |
} |
|
| 82 |
} |
|
| 83 | ||
| 84 | ! |
benchmark_results$benchmarks$individual <- individual_benchmarks |
| 85 | ||
| 86 |
# Benchmark 2: Batch processing |
|
| 87 | ||
| 88 |
# nolint: object_usage_linter |
|
| 89 | ! |
batch_time <- microbenchmark::microbenchmark( |
| 90 | ! |
process_ideal_course_batch(), |
| 91 | ! |
times = iterations |
| 92 |
) |
|
| 93 | ||
| 94 | ! |
batch_memory <- NULL |
| 95 | ! |
if (include_memory) {
|
| 96 |
# nolint: object_usage_linter |
|
| 97 | ! |
batch_memory <- measure_memory_usage( |
| 98 | ! |
process_ideal_course_batch() |
| 99 |
) |
|
| 100 |
} |
|
| 101 | ||
| 102 | ! |
benchmark_results$benchmarks$batch <- list( |
| 103 | ! |
processing_time = batch_time, |
| 104 | ! |
memory_usage = batch_memory |
| 105 |
) |
|
| 106 | ||
| 107 |
# Benchmark 3: Function-specific benchmarks |
|
| 108 | ||
| 109 |
# Test with first session |
|
| 110 | ! |
test_file <- file.path(transcript_dir, session_files[1]) |
| 111 | ||
| 112 | ! |
function_benchmarks <- list( |
| 113 | ! |
load_transcript = microbenchmark::microbenchmark( |
| 114 | ! |
load_zoom_transcript(test_file), |
| 115 | ! |
times = iterations |
| 116 |
), |
|
| 117 | ! |
summarize_metrics = microbenchmark::microbenchmark( |
| 118 | ! |
summarize_transcript_metrics(test_file), |
| 119 | ! |
times = iterations |
| 120 |
), |
|
| 121 | ! |
consolidate_transcript = microbenchmark::microbenchmark( |
| 122 | ! |
consolidate_transcript(load_zoom_transcript(test_file)), |
| 123 | ! |
times = iterations |
| 124 |
) |
|
| 125 |
) |
|
| 126 | ||
| 127 | ! |
benchmark_results$benchmarks$functions <- function_benchmarks |
| 128 | ||
| 129 |
# Generate summary statistics |
|
| 130 | ! |
benchmark_results$summary <- generate_benchmark_summary(benchmark_results) |
| 131 | ||
| 132 |
# Save results |
|
| 133 | ! |
if (!is.null(output_file)) {
|
| 134 | ! |
saveRDS(benchmark_results, output_file) |
| 135 |
} |
|
| 136 | ||
| 137 |
# Print summary |
|
| 138 | ! |
print_benchmark_summary(benchmark_results$summary) |
| 139 | ||
| 140 | ! |
benchmark_results |
| 141 |
} |
|
| 142 | ||
| 143 |
#' Measure memory usage of a function |
|
| 144 |
#' @keywords internal |
|
| 145 |
measure_memory_usage <- function(expr) {
|
|
| 146 |
# DEPRECATED: This function will be removed in the next version |
|
| 147 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 148 | ! |
warning("Function 'measure_memory_usage' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 149 | ||
| 150 | ! |
if (!requireNamespace("pryr", quietly = TRUE)) {
|
| 151 | ! |
return(NULL) |
| 152 |
} |
|
| 153 | ||
| 154 | ! |
mem_before <- pryr::mem_used() |
| 155 | ! |
result <- eval(expr) |
| 156 | ! |
mem_after <- pryr::mem_used() |
| 157 | ||
| 158 | ! |
list( |
| 159 | ! |
memory_before = mem_before, |
| 160 | ! |
memory_after = mem_after, |
| 161 | ! |
memory_increase = mem_after - mem_before, |
| 162 | ! |
result_size = pryr::object_size(result) |
| 163 |
) |
|
| 164 |
} |
|
| 165 | ||
| 166 |
#' Generate benchmark summary statistics |
|
| 167 |
#' @keywords internal |
|
| 168 | ||
| 169 |
generate_benchmark_summary <- function(benchmark_results) {
|
|
| 170 |
# DEPRECATED: This function will be removed in the next version |
|
| 171 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 172 | ! |
warning("Function 'generate_benchmark_summary' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 173 | ||
| 174 | ! |
summary <- list() |
| 175 | ||
| 176 |
# Individual processing summary |
|
| 177 | ! |
if ("individual" %in% names(benchmark_results$benchmarks)) {
|
| 178 | ! |
individual_times <- sapply( |
| 179 | ! |
benchmark_results$benchmarks$individual, |
| 180 | ! |
function(x) summary(x$processing_time)$median |
| 181 |
) |
|
| 182 | ||
| 183 | ! |
summary$individual_processing <- list( |
| 184 | ! |
mean_time = mean(individual_times), |
| 185 | ! |
median_time = stats::median(individual_times), |
| 186 | ! |
min_time = min(individual_times), |
| 187 | ! |
max_time = max(individual_times), |
| 188 | ! |
total_files = length(individual_times) |
| 189 |
) |
|
| 190 |
} |
|
| 191 | ||
| 192 |
# Batch processing summary |
|
| 193 | ! |
if ("batch" %in% names(benchmark_results$benchmarks)) {
|
| 194 | ! |
batch_time <- summary(benchmark_results$benchmarks$batch$processing_time)$median |
| 195 | ||
| 196 | ! |
summary$batch_processing <- list( |
| 197 | ! |
median_time = batch_time, |
| 198 | ! |
throughput = 3 / batch_time # files per second |
| 199 |
) |
|
| 200 |
} |
|
| 201 | ||
| 202 | ! |
summary |
| 203 |
} |
|
| 204 | ||
| 205 |
#' Print benchmark summary |
|
| 206 |
#' @keywords internal |
|
| 207 |
print_benchmark_summary <- function(summary) {
|
|
| 208 |
# DEPRECATED: This function will be removed in the next version |
|
| 209 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 210 | ! |
warning("Function 'print_benchmark_summary' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 211 | ||
| 212 |
# Silent function - no diagnostic output |
|
| 213 | ! |
invisible(summary) |
| 214 |
} |
| 1 |
#' Validate Privacy Compliance |
|
| 2 |
#' |
|
| 3 |
#' Scans data objects to ensure no real names appear in outputs when privacy |
|
| 4 |
#' masking is enabled. This function performs exact matching to detect privacy |
|
| 5 |
#' violations and stops processing if real names are found. |
|
| 6 |
#' |
|
| 7 |
#' @param data Data object to validate (data.frame, tibble, or list) |
|
| 8 |
#' @param privacy_level Privacy level to validate against. One of |
|
| 9 |
#' `c("ferpa_strict", "ferpa_standard", "mask", "none")`.
|
|
| 10 |
#' Defaults to `getOption("zoomstudentengagement.privacy_level", "mask")`.
|
|
| 11 |
#' @param real_names Character vector of real names to check against. |
|
| 12 |
#' If NULL, uses common name patterns to detect potential violations. |
|
| 13 |
#' @param stop_on_violation Logical, whether to stop with error on privacy violation. |
|
| 14 |
#' Defaults to TRUE for maximum privacy protection. |
|
| 15 |
#' |
|
| 16 |
#' @return TRUE if compliant, error if violation found and stop_on_violation = TRUE |
|
| 17 |
#' @export |
|
| 18 |
#' |
|
| 19 |
#' @examples |
|
| 20 |
#' # Validate privacy compliance |
|
| 21 |
#' df <- tibble::tibble( |
|
| 22 |
#' name = c("Student_01", "Student_02"),
|
|
| 23 |
#' score = c(85, 92) |
|
| 24 |
#' ) |
|
| 25 |
#' validate_privacy_compliance(df) |
|
| 26 |
#' |
|
| 27 |
#' # Check with specific real names |
|
| 28 |
#' real_names <- c("John Smith", "Jane Doe")
|
|
| 29 |
#' validate_privacy_compliance(df, real_names = real_names) |
|
| 30 |
validate_privacy_compliance <- function(data = NULL, |
|
| 31 |
privacy_level = getOption( |
|
| 32 |
"zoomstudentengagement.privacy_level", |
|
| 33 |
"mask" |
|
| 34 |
), |
|
| 35 |
real_names = NULL, |
|
| 36 |
stop_on_violation = TRUE) {
|
|
| 37 |
# Validate inputs |
|
| 38 | 26x |
valid_levels <- c("ferpa_strict", "ferpa_standard", "mask", "none")
|
| 39 | 26x |
if (!privacy_level %in% valid_levels) {
|
| 40 | 1x |
stop("Invalid privacy_level. Must be one of: ",
|
| 41 | 1x |
paste(valid_levels, collapse = ", "), |
| 42 | 1x |
call. = FALSE |
| 43 |
) |
|
| 44 |
} |
|
| 45 | ||
| 46 | 25x |
if (!is.logical(stop_on_violation) || length(stop_on_violation) != 1) {
|
| 47 | 1x |
stop("stop_on_violation must be a single logical value", call. = FALSE)
|
| 48 |
} |
|
| 49 | ||
| 50 |
# If privacy is disabled, always return TRUE |
|
| 51 | 24x |
if (identical(privacy_level, "none")) {
|
| 52 | 6x |
return(TRUE) |
| 53 |
} |
|
| 54 | ||
| 55 |
# If no data provided, return TRUE |
|
| 56 | 18x |
if (is.null(data)) {
|
| 57 | 1x |
return(TRUE) |
| 58 |
} |
|
| 59 | ||
| 60 |
# Extract all character values from the data |
|
| 61 | 17x |
character_values <- extract_character_values(data) |
| 62 | ||
| 63 |
# If no character values found, return TRUE |
|
| 64 | 17x |
if (length(character_values) == 0) {
|
| 65 | 1x |
return(TRUE) |
| 66 |
} |
|
| 67 | ||
| 68 |
# Check for privacy violations |
|
| 69 | 16x |
violations <- detect_privacy_violations( |
| 70 | 16x |
character_values, |
| 71 | 16x |
real_names, |
| 72 | 16x |
privacy_level |
| 73 |
) |
|
| 74 | ||
| 75 |
# If violations found, handle according to stop_on_violation |
|
| 76 | 16x |
if (length(violations) > 0) {
|
| 77 | 3x |
violation_msg <- paste( |
| 78 | 3x |
"Privacy violation detected:", |
| 79 | 3x |
"Real names found in output data:", |
| 80 | 3x |
paste(violations, collapse = ", "), |
| 81 | 3x |
"\nThis indicates a bug in the privacy implementation.", |
| 82 | 3x |
"Please report this issue immediately." |
| 83 |
) |
|
| 84 | ||
| 85 | 3x |
if (stop_on_violation) {
|
| 86 | 2x |
stop(violation_msg, call. = FALSE) |
| 87 |
} else {
|
|
| 88 | 1x |
warning(violation_msg, call. = FALSE) |
| 89 |
} |
|
| 90 |
} |
|
| 91 | ||
| 92 |
# Return TRUE if no violations |
|
| 93 | 14x |
TRUE |
| 94 |
} |
|
| 95 | ||
| 96 |
#' Extract Character Values from Data Object |
|
| 97 |
#' |
|
| 98 |
#' Internal function to extract all character values from various data types |
|
| 99 |
#' for privacy validation. |
|
| 100 |
#' |
|
| 101 |
#' @param data Data object to extract character values from |
|
| 102 |
#' |
|
| 103 |
#' @return Character vector of all character values found |
|
| 104 |
#' @keywords internal |
|
| 105 |
extract_character_values <- function(data) {
|
|
| 106 |
# DEPRECATED: This function will be removed in the next version |
|
| 107 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 108 | 25x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 109 | ! |
warning("Function 'extract_character_values' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 110 |
} |
|
| 111 | ||
| 112 |
# Handle different data types |
|
| 113 | 25x |
if (is.data.frame(data)) {
|
| 114 |
# Extract character columns |
|
| 115 | 20x |
char_cols <- sapply(data, is.character) |
| 116 | 20x |
if (any(char_cols)) {
|
| 117 | 19x |
values <- unlist(data[char_cols], use.names = FALSE) |
| 118 | 19x |
return(values[!is.na(values) & nchar(trimws(values)) > 0]) |
| 119 |
} |
|
| 120 | 5x |
} else if (is.list(data)) {
|
| 121 |
# Recursively extract from list elements |
|
| 122 | 2x |
values <- unlist(lapply(data, extract_character_values), use.names = FALSE) |
| 123 | 2x |
return(values[!is.na(values) & nchar(trimws(values)) > 0]) |
| 124 | 3x |
} else if (is.character(data)) {
|
| 125 |
# Direct character vector |
|
| 126 | 2x |
return(data[!is.na(data) & nchar(trimws(data)) > 0]) |
| 127 |
} |
|
| 128 | ||
| 129 |
# Return empty character vector for unsupported types |
|
| 130 | 2x |
character(0) |
| 131 |
} |
|
| 132 | ||
| 133 |
#' Detect Privacy Violations |
|
| 134 |
#' |
|
| 135 |
#' Internal function to detect privacy violations by checking for real names |
|
| 136 |
#' in character values. |
|
| 137 |
#' |
|
| 138 |
#' @param character_values Character vector to check |
|
| 139 |
#' @param real_names Specific real names to check against (optional) |
|
| 140 |
#' @param privacy_level Privacy level for validation |
|
| 141 |
#' |
|
| 142 |
#' @return Character vector of detected violations |
|
| 143 |
#' @keywords internal |
|
| 144 |
detect_privacy_violations <- function(character_values, real_names, privacy_level) {
|
|
| 145 |
# DEPRECATED: This function will be removed in the next version |
|
| 146 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 147 | 21x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 148 | ! |
warning("Function 'detect_privacy_violations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 149 |
} |
|
| 150 | ||
| 151 | 21x |
violations <- character(0) |
| 152 | ||
| 153 |
# If specific real names provided, check for exact matches |
|
| 154 | 21x |
if (!is.null(real_names) && length(real_names) > 0) {
|
| 155 |
# Normalize both sets for comparison |
|
| 156 | 10x |
normalized_values <- normalize_name_for_matching(character_values) |
| 157 | 10x |
normalized_names <- normalize_name_for_matching(real_names) |
| 158 | ||
| 159 |
# Find exact matches |
|
| 160 | 10x |
matches <- normalized_values %in% normalized_names |
| 161 | 10x |
if (any(matches)) {
|
| 162 | 3x |
violations <- c(violations, character_values[matches]) |
| 163 |
} |
|
| 164 |
} |
|
| 165 | ||
| 166 |
# Check for common name patterns that might indicate privacy violations |
|
| 167 |
# This is a conservative approach - only flag obvious violations |
|
| 168 | 21x |
name_patterns <- c( |
| 169 |
# Common name patterns (first + last) - more specific to avoid false positives |
|
| 170 | 21x |
"^[A-Z][a-z]{1,20}\\s+[A-Z][a-z]{1,20}$",
|
| 171 |
# Names with titles |
|
| 172 | 21x |
"^(Dr|Prof|Professor|Mr|Mrs|Ms|Miss)\\.?\\s+[A-Z][a-z]{1,20}\\s+[A-Z][a-z]{1,20}$",
|
| 173 |
# Names with middle initials |
|
| 174 | 21x |
"^[A-Z][a-z]{1,20}\\s+[A-Z]\\.\\s+[A-Z][a-z]{1,20}$"
|
| 175 |
) |
|
| 176 | ||
| 177 | 21x |
for (pattern in name_patterns) {
|
| 178 | 63x |
matches <- grepl(pattern, character_values, perl = TRUE) |
| 179 | 63x |
if (any(matches)) {
|
| 180 |
# Only flag if they don't look like masked names |
|
| 181 | 6x |
potential_violations <- character_values[matches] |
| 182 |
# Exclude obvious masked names (Student_XX, etc.) |
|
| 183 | 6x |
not_masked <- !grepl("^(Student|Guest|Instructor)_\\d+$", potential_violations)
|
| 184 | ||
| 185 |
# Exclude common non-name phrases that might match patterns |
|
| 186 | 6x |
common_phrases <- c( |
| 187 | 6x |
"World Testing Report", "Test Results Summary", "Test Report", |
| 188 | 6x |
"Data Analysis", "Report Summary", "Analysis Results", |
| 189 | 6x |
"Testing Report", "Results Summary", "Summary Report", |
| 190 | 6x |
"Test Summary", "Report Results", "Analysis Report" |
| 191 |
) |
|
| 192 | 6x |
not_common_phrase <- !potential_violations %in% common_phrases |
| 193 | ||
| 194 |
# Only flag if both conditions are met |
|
| 195 | 6x |
final_violations <- potential_violations[not_masked & not_common_phrase] |
| 196 | 6x |
if (length(final_violations) > 0) {
|
| 197 | 6x |
violations <- c(violations, final_violations) |
| 198 |
} |
|
| 199 |
} |
|
| 200 |
} |
|
| 201 | ||
| 202 |
# Remove duplicates and return |
|
| 203 | 21x |
unique(violations) |
| 204 |
} |
| 1 |
#' Join Transcripts Files Into a Single Tibble |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from the joining of the listing of session recordings loaded from the cloud recording csvs |
|
| 4 |
#' (`df_zoom_recorded_sessions`), the list of transcript files |
|
| 5 |
#' (`df_transcript_files`), and the list of cancelled classes |
|
| 6 |
#' (`df_cancelled_classes`) into a single tibble |
|
| 7 |
#' |
|
| 8 |
#' @param df_zoom_recorded_sessions A tibble listing the session recordings |
|
| 9 |
#' loaded from the cloud recording csvs. |
|
| 10 |
#' @param df_transcript_files A data.frame listing the transcript files from the |
|
| 11 |
#' zoom recordings loaded from the cloud recording csvs and transcripts. |
|
| 12 |
#' @param df_cancelled_classes A tibble listing the cancelled class sessions for |
|
| 13 |
#' scheduled classes where a zoom recording is not expected. |
|
| 14 |
#' |
|
| 15 |
#' @return A tibble listing the the class sessions with corresponding transcript |
|
| 16 |
#' files or placeholders for cancelled classes. |
|
| 17 |
#' @export |
|
| 18 |
#' @keywords deprecated |
|
| 19 |
#' |
|
| 20 |
#' @examples |
|
| 21 |
#' \dontrun{
|
|
| 22 |
#' zoom_recorded_sessions_df <- load_zoom_recorded_sessions_list() |
|
| 23 |
#' transcript_files_df <- load_transcript_files_list() |
|
| 24 |
#' cancelled_classes_df <- load_cancelled_classes() |
|
| 25 |
#' |
|
| 26 |
#' join_transcripts_list( |
|
| 27 |
#' df_zoom_recorded_sessions = zoom_recorded_sessions_df, |
|
| 28 |
#' df_transcript_files = transcript_files_df, |
|
| 29 |
#' df_cancelled_classes = cancelled_classes_df |
|
| 30 |
#' ) |
|
| 31 |
#' } |
|
| 32 |
join_transcripts_list <- function( |
|
| 33 |
df_zoom_recorded_sessions = NULL, |
|
| 34 |
df_transcript_files = NULL, |
|
| 35 |
df_cancelled_classes = NULL) {
|
|
| 36 |
# DEPRECATED: This function will be removed in the next version |
|
| 37 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 38 | 8x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 39 | ! |
warning("Function 'join_transcripts_list' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 40 |
} |
|
| 41 | ||
| 42 | 8x |
match_start_time <- start_time_local <- match_end_time <- section <- NULL |
| 43 | ||
| 44 |
# Return empty tibble with correct structure if any input is invalid |
|
| 45 | 8x |
if (!tibble::is_tibble(df_zoom_recorded_sessions) || |
| 46 | 8x |
!tibble::is_tibble(df_transcript_files) || |
| 47 | 8x |
!tibble::is_tibble(df_cancelled_classes)) {
|
| 48 | 2x |
return(tibble::tibble( |
| 49 | 2x |
section = character(), |
| 50 | 2x |
match_start_time = as.POSIXct(character()), |
| 51 | 2x |
match_end_time = as.POSIXct(character()), |
| 52 | 2x |
start_time_local = as.POSIXct(character()), |
| 53 | 2x |
session_num = integer() |
| 54 |
)) |
|
| 55 |
} |
|
| 56 | ||
| 57 |
# Return empty tibble if any required column is missing |
|
| 58 | 6x |
zoom_recorded_sessions_required_cols <- c("section", "match_start_time", "match_end_time")
|
| 59 | 6x |
transcript_files_required_cols <- c("start_time_local")
|
| 60 | ||
| 61 | 6x |
if (!all(zoom_recorded_sessions_required_cols %in% names(df_zoom_recorded_sessions) & transcript_files_required_cols %in% names(df_transcript_files))) {
|
| 62 | 2x |
return(tibble::tibble( |
| 63 | 2x |
section = character(), |
| 64 | 2x |
match_start_time = as.POSIXct(character()), |
| 65 | 2x |
match_end_time = as.POSIXct(character()), |
| 66 | 2x |
start_time_local = as.POSIXct(character()), |
| 67 | 2x |
session_num = integer() |
| 68 |
)) |
|
| 69 |
} |
|
| 70 | ||
| 71 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 72 |
# Create cross join manually |
|
| 73 | 4x |
joined_sessions <- expand.grid( |
| 74 | 4x |
i = seq_len(nrow(df_zoom_recorded_sessions)), |
| 75 | 4x |
j = seq_len(nrow(df_transcript_files)) |
| 76 |
) |
|
| 77 | ||
| 78 |
# Extract rows from both data frames |
|
| 79 | 4x |
zoom_rows <- df_zoom_recorded_sessions[joined_sessions$i, , drop = FALSE] |
| 80 | 4x |
transcript_rows <- df_transcript_files[joined_sessions$j, , drop = FALSE] |
| 81 | ||
| 82 |
# Combine columns |
|
| 83 | 4x |
joined_sessions <- cbind(zoom_rows, transcript_rows) |
| 84 | ||
| 85 |
# Filter using base R instead of dplyr |
|
| 86 | 4x |
filter_condition <- joined_sessions$match_start_time <= joined_sessions$start_time_local & |
| 87 | 4x |
joined_sessions$match_end_time >= joined_sessions$start_time_local |
| 88 | 4x |
joined_sessions <- joined_sessions[filter_condition, , drop = FALSE] |
| 89 | ||
| 90 |
# Get all columns needed in the final output |
|
| 91 | 4x |
all_cols <- union(names(joined_sessions), names(df_cancelled_classes)) |
| 92 | ||
| 93 |
# Add missing columns as NA, matching type from reference data frame |
|
| 94 | 4x |
add_missing_cols <- function(df, all_cols, ref_df) {
|
| 95 | 8x |
for (col in setdiff(all_cols, names(df))) {
|
| 96 | 11x |
if (col %in% names(ref_df)) {
|
| 97 |
# Match type from reference data frame |
|
| 98 | 11x |
ref_col <- ref_df[[col]] |
| 99 | 11x |
if (inherits(ref_col, "POSIXct")) {
|
| 100 |
# Use same timezone as reference |
|
| 101 | 6x |
tz <- attr(ref_col, "tzone") |
| 102 | ! |
if (is.null(tz)) tz <- "UTC" |
| 103 | 6x |
df[[col]] <- as.POSIXct(NA, tz = tz) |
| 104 | 5x |
} else if (is.numeric(ref_col)) {
|
| 105 | ! |
df[[col]] <- as.numeric(NA) |
| 106 | 5x |
} else if (is.character(ref_col)) {
|
| 107 | 5x |
df[[col]] <- as.character(NA) |
| 108 | ! |
} else if (is.integer(ref_col)) {
|
| 109 | ! |
df[[col]] <- as.integer(NA) |
| 110 | ! |
} else if (is.logical(ref_col)) {
|
| 111 | ! |
df[[col]] <- as.logical(NA) |
| 112 |
} else {
|
|
| 113 | ! |
df[[col]] <- NA |
| 114 |
} |
|
| 115 |
} else {
|
|
| 116 |
# Default to logical NA |
|
| 117 | ! |
df[[col]] <- NA |
| 118 |
} |
|
| 119 |
} |
|
| 120 | 8x |
df[all_cols] |
| 121 |
} |
|
| 122 | ||
| 123 | 4x |
joined_sessions <- add_missing_cols(joined_sessions, all_cols, df_cancelled_classes) |
| 124 | 4x |
df_cancelled_classes <- add_missing_cols(df_cancelled_classes, all_cols, joined_sessions) |
| 125 | ||
| 126 |
# Coerce 'section' to character in both data frames to avoid type mismatch |
|
| 127 | 4x |
joined_sessions$section <- as.character(joined_sessions$section) |
| 128 | 4x |
df_cancelled_classes$section <- as.character(df_cancelled_classes$section) |
| 129 | ||
| 130 |
# Coerce 'course_section' to character in both data frames to avoid type mismatch |
|
| 131 | 4x |
if ("course_section" %in% names(joined_sessions)) {
|
| 132 | 1x |
joined_sessions$course_section <- as.character(joined_sessions$course_section) |
| 133 |
} |
|
| 134 | 4x |
if ("course_section" %in% names(df_cancelled_classes)) {
|
| 135 | 1x |
df_cancelled_classes$course_section <- as.character(df_cancelled_classes$course_section) |
| 136 |
} |
|
| 137 | ||
| 138 |
# Coerce 'ID' to character in both data frames to avoid type mismatch |
|
| 139 | 4x |
if ("ID" %in% names(joined_sessions)) {
|
| 140 | 1x |
joined_sessions$ID <- as.character(joined_sessions$ID) |
| 141 |
} |
|
| 142 | 4x |
if ("ID" %in% names(df_cancelled_classes)) {
|
| 143 | 1x |
df_cancelled_classes$ID <- as.character(df_cancelled_classes$ID) |
| 144 |
} |
|
| 145 | ||
| 146 |
# Coerce file columns to character in both data frames to avoid type mismatch |
|
| 147 | 4x |
file_cols <- c("chat_file", "transcript_file", "closed_caption_file")
|
| 148 | 4x |
for (col in file_cols) {
|
| 149 | 12x |
if (col %in% names(joined_sessions)) {
|
| 150 | 6x |
joined_sessions[[col]] <- as.character(joined_sessions[[col]]) |
| 151 |
} |
|
| 152 | 12x |
if (col %in% names(df_cancelled_classes)) {
|
| 153 | 6x |
df_cancelled_classes[[col]] <- as.character(df_cancelled_classes[[col]]) |
| 154 |
} |
|
| 155 |
} |
|
| 156 | ||
| 157 |
# Coerce 'Start Time' to character in both data frames to avoid type mismatch |
|
| 158 | 4x |
if ("Start Time" %in% names(joined_sessions)) {
|
| 159 | 1x |
joined_sessions$`Start Time` <- as.character(joined_sessions$`Start Time`) |
| 160 |
} |
|
| 161 | 4x |
if ("Start Time" %in% names(df_cancelled_classes)) {
|
| 162 | 1x |
df_cancelled_classes$`Start Time` <- as.character(df_cancelled_classes$`Start Time`) |
| 163 |
} |
|
| 164 | ||
| 165 |
# Coerce numeric columns to numeric in both data frames to avoid type mismatch |
|
| 166 | 4x |
numeric_cols <- c("Total Views", "Total Downloads", "File Count", "File Size (MB)")
|
| 167 | 4x |
for (col in numeric_cols) {
|
| 168 | 16x |
if (col %in% names(joined_sessions)) {
|
| 169 | 2x |
joined_sessions[[col]] <- suppressWarnings(as.numeric(joined_sessions[[col]])) |
| 170 |
} |
|
| 171 | 16x |
if (col %in% names(df_cancelled_classes)) {
|
| 172 | 2x |
df_cancelled_classes[[col]] <- suppressWarnings(as.numeric(df_cancelled_classes[[col]])) |
| 173 |
} |
|
| 174 |
} |
|
| 175 | ||
| 176 |
# Coerce 'Last Accessed' to character in both data frames to avoid type mismatch |
|
| 177 | 4x |
if ("Last Accessed" %in% names(joined_sessions)) {
|
| 178 | 1x |
joined_sessions$`Last Accessed` <- as.character(joined_sessions$`Last Accessed`) |
| 179 |
} |
|
| 180 | 4x |
if ("Last Accessed" %in% names(df_cancelled_classes)) {
|
| 181 | 1x |
df_cancelled_classes$`Last Accessed` <- as.character(df_cancelled_classes$`Last Accessed`) |
| 182 |
} |
|
| 183 | ||
| 184 |
# Coerce 'match_start_time' and 'match_end_time' to character in both data frames to avoid type mismatch |
|
| 185 | 4x |
for (col in c("match_start_time", "match_end_time")) {
|
| 186 | 8x |
if (col %in% names(joined_sessions)) {
|
| 187 | 8x |
joined_sessions[[col]] <- as.character(joined_sessions[[col]]) |
| 188 |
} |
|
| 189 | 8x |
if (col %in% names(df_cancelled_classes)) {
|
| 190 | 8x |
df_cancelled_classes[[col]] <- as.character(df_cancelled_classes[[col]]) |
| 191 |
} |
|
| 192 |
} |
|
| 193 | ||
| 194 |
# Coerce 'date_extract' to character in both data frames to avoid type mismatch |
|
| 195 | 4x |
if ("date_extract" %in% names(joined_sessions)) {
|
| 196 | 1x |
joined_sessions$date_extract <- as.character(joined_sessions$date_extract) |
| 197 |
} |
|
| 198 | 4x |
if ("date_extract" %in% names(df_cancelled_classes)) {
|
| 199 | 1x |
df_cancelled_classes$date_extract <- as.character(df_cancelled_classes$date_extract) |
| 200 |
} |
|
| 201 | ||
| 202 |
# Coerce 'recording_start' to character in both data frames to avoid type mismatch |
|
| 203 | 4x |
if ("recording_start" %in% names(joined_sessions)) {
|
| 204 | 1x |
joined_sessions$recording_start <- as.character(joined_sessions$recording_start) |
| 205 |
} |
|
| 206 | 4x |
if ("recording_start" %in% names(df_cancelled_classes)) {
|
| 207 | 1x |
df_cancelled_classes$recording_start <- as.character(df_cancelled_classes$recording_start) |
| 208 |
} |
|
| 209 | ||
| 210 |
# Coerce 'start_time_local' to character in both data frames to avoid type mismatch |
|
| 211 | 4x |
if ("start_time_local" %in% names(joined_sessions)) {
|
| 212 | 4x |
joined_sessions$start_time_local <- as.character(joined_sessions$start_time_local) |
| 213 |
} |
|
| 214 | 4x |
if ("start_time_local" %in% names(df_cancelled_classes)) {
|
| 215 | 4x |
df_cancelled_classes$start_time_local <- as.character(df_cancelled_classes$start_time_local) |
| 216 |
} |
|
| 217 | ||
| 218 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 219 |
# Combine the data frames |
|
| 220 | 4x |
result <- rbind(joined_sessions, df_cancelled_classes) |
| 221 | ||
| 222 |
# Sort by start_time_local |
|
| 223 | 4x |
if ("start_time_local" %in% names(result)) {
|
| 224 | 4x |
result <- result[order(result$start_time_local), , drop = FALSE] |
| 225 |
} |
|
| 226 | ||
| 227 |
# Add session_num by section using base R |
|
| 228 | 4x |
if ("section" %in% names(result) && "start_time_local" %in% names(result)) {
|
| 229 |
# Convert start_time_local back to POSIXct for proper ordering |
|
| 230 | 4x |
result$start_time_local <- as.POSIXct(result$start_time_local) |
| 231 | ||
| 232 |
# Calculate session_num by section |
|
| 233 | 4x |
result$session_num <- NA_integer_ |
| 234 | 4x |
sections <- unique(result$section) |
| 235 | ||
| 236 | 4x |
for (sect in sections) {
|
| 237 | 10x |
if (!is.na(sect)) { # Handle NA sections
|
| 238 | 8x |
section_rows <- result$section == sect |
| 239 | 8x |
if (sum(section_rows, na.rm = TRUE) > 0) {
|
| 240 |
# Get the order within this section |
|
| 241 | 8x |
section_data <- result[section_rows, , drop = FALSE] |
| 242 | 8x |
section_order <- order(section_data$start_time_local) |
| 243 | ||
| 244 |
# Assign dense rank (1, 2, 3, etc.) - handle NA rows carefully |
|
| 245 | 8x |
valid_rows <- which(section_rows) |
| 246 | 8x |
if (length(valid_rows) > 0) {
|
| 247 | 8x |
result$session_num[valid_rows[section_order]] <- seq_len(length(valid_rows)) |
| 248 |
} |
|
| 249 |
} |
|
| 250 |
} |
|
| 251 |
} |
|
| 252 |
} |
|
| 253 | ||
| 254 |
# Convert to tibble to maintain expected return type |
|
| 255 | 4x |
tibble::as_tibble(result) |
| 256 |
} |
|
| 257 |
# join_transcripts_list(df_zoom_recorded_sessions = zoom_recorded_sessions_df, |
|
| 258 |
# df_transcript_files = transcript_files_df, |
|
| 259 |
# df_cancelled_classes = cancelled_classes_df) |
| 1 |
#' Classify Participants (Pure, Privacy-Aware) |
|
| 2 |
#' |
|
| 3 |
#' Given transcript utterances, a student roster, and an optional lookup, |
|
| 4 |
#' returns a data frame with classification columns that identify each |
|
| 5 |
#' utterance's `clean_name`, `participant_type`, `student_id`, and |
|
| 6 |
#' `is_matched`. This function is pure and performs no filesystem writes. |
|
| 7 |
#' |
|
| 8 |
#' Privacy defaults are applied to outputs. Use this to classify participants |
|
| 9 |
#' BEFORE computing metrics so that downstream functions operate on a |
|
| 10 |
#' privacy-safe, idempotent representation. |
|
| 11 |
#' |
|
| 12 |
#' @param transcript_df Data frame of transcript utterances. |
|
| 13 |
#' Must contain one of: `transcript_name`, `name`, `speaker_name`, `participant_name`. |
|
| 14 |
#' @param roster_df Data frame of enrolled students. |
|
| 15 |
#' Should contain one of: `first_last`, `preferred_name`, `formal_name`, `name`, `student_name`, |
|
| 16 |
#' and optionally `student_id`. |
|
| 17 |
#' @param lookup_df Optional data frame of name mappings as produced by |
|
| 18 |
#' `read_lookup_safely()`. |
|
| 19 |
#' @param privacy_level One of `c("ferpa_strict", "ferpa_standard", "mask", "none")`.
|
|
| 20 |
#' Defaults to `getOption("zoomstudentengagement.privacy_level", "mask")`.
|
|
| 21 |
#' |
|
| 22 |
#' @return The `transcript_df` augmented with columns: |
|
| 23 |
#' `clean_name`, `participant_type`, `student_id`, `is_matched`. |
|
| 24 |
#' @export |
|
| 25 |
#' @keywords deprecated |
|
| 26 |
classify_participants <- function(transcript_df = NULL, |
|
| 27 |
roster_df = NULL, |
|
| 28 |
lookup_df = NULL, |
|
| 29 |
privacy_level = getOption( |
|
| 30 |
"zoomstudentengagement.privacy_level", |
|
| 31 |
"mask" |
|
| 32 |
)) {
|
|
| 33 |
# DEPRECATED: This function will be removed in the next version |
|
| 34 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 35 | 3x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 36 | ! |
warning("Function 'classify_participants' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 37 |
} |
|
| 38 | ||
| 39 |
# Validate inputs |
|
| 40 | 3x |
if (!is.data.frame(transcript_df)) {
|
| 41 | ! |
stop("transcript_df must be a data frame", call. = FALSE)
|
| 42 |
} |
|
| 43 | 3x |
if (!is.data.frame(roster_df)) {
|
| 44 | ! |
stop("roster_df must be a data frame", call. = FALSE)
|
| 45 |
} |
|
| 46 | ||
| 47 | 3x |
valid_levels <- c("ferpa_strict", "ferpa_standard", "mask", "none")
|
| 48 | 3x |
if (!privacy_level %in% valid_levels) {
|
| 49 | ! |
stop("Invalid privacy_level. Must be one of: ",
|
| 50 | ! |
paste(valid_levels, collapse = ", "), |
| 51 | ! |
call. = FALSE |
| 52 |
) |
|
| 53 |
} |
|
| 54 | ||
| 55 |
# Ensure transcript has a name column |
|
| 56 | 3x |
transcript_name_columns <- c("transcript_name", "name", "speaker_name", "participant_name")
|
| 57 | 3x |
name_col <- intersect(transcript_name_columns, names(transcript_df)) |
| 58 | 3x |
if (length(name_col) == 0) {
|
| 59 | ! |
stop("Transcript data lacks a usable name column", call. = FALSE)
|
| 60 |
} |
|
| 61 | 3x |
name_col <- name_col[1] |
| 62 | ||
| 63 |
# Extract names |
|
| 64 | 3x |
extract_transcript_names <- function(df) {
|
| 65 | 3x |
vals <- df[[name_col]] |
| 66 | ! |
if (is.factor(vals)) vals <- as.character(vals) |
| 67 | 3x |
enc2utf8(as.character(vals)) |
| 68 |
} |
|
| 69 | 3x |
transcript_names <- extract_transcript_names(transcript_df) |
| 70 | ||
| 71 |
# Build roster name vector |
|
| 72 | 3x |
roster_name_columns <- c("first_last", "preferred_name", "formal_name", "name", "student_name")
|
| 73 | 3x |
roster_name_col <- intersect(roster_name_columns, names(roster_df)) |
| 74 | 3x |
if (length(roster_name_col) == 0 || nrow(roster_df) == 0) {
|
| 75 | ! |
stop("Roster appears empty or lacks required name columns.", call. = FALSE)
|
| 76 |
} |
|
| 77 | 3x |
roster_name_col <- roster_name_col[1] |
| 78 | 3x |
roster_names <- enc2utf8(as.character(roster_df[[roster_name_col]])) |
| 79 | ||
| 80 |
# Normalize lookup if provided |
|
| 81 | 3x |
if (!is.null(lookup_df)) {
|
| 82 | 2x |
lookup_df <- tryCatch(.normalize_lookup_df(lookup_df), error = function(e) lookup_df) |
| 83 |
} |
|
| 84 | ||
| 85 |
# Create lookup table: existing mappings take precedence |
|
| 86 | 3x |
create_lookup <- function(transcript_names, roster_names, lookup_df) {
|
| 87 | 3x |
base <- data.frame( |
| 88 | 3x |
transcript_name = enc2utf8(as.character(transcript_names)), |
| 89 | 3x |
preferred_name = NA_character_, |
| 90 | 3x |
formal_name = NA_character_, |
| 91 | 3x |
participant_type = NA_character_, |
| 92 | 3x |
student_id = NA_character_, |
| 93 | 3x |
stringsAsFactors = FALSE |
| 94 |
) |
|
| 95 | 3x |
if (!is.null(lookup_df) && nrow(lookup_df) > 0) {
|
| 96 | 2x |
for (i in seq_len(nrow(base))) {
|
| 97 | 6x |
nm <- base$transcript_name[i] |
| 98 | 6x |
idx <- which(lookup_df$transcript_name == nm) |
| 99 | 6x |
if (length(idx) > 0) {
|
| 100 | 2x |
m <- lookup_df[idx[1], ] |
| 101 | 2x |
base$preferred_name[i] <- m$preferred_name |
| 102 | 2x |
base$formal_name[i] <- m$formal_name |
| 103 | 2x |
base$participant_type[i] <- m$participant_type |
| 104 | 2x |
base$student_id[i] <- m$student_id |
| 105 |
} |
|
| 106 |
} |
|
| 107 |
} |
|
| 108 |
# Fill from roster exact matches where still missing |
|
| 109 | 3x |
normalize_name <- function(x) {
|
| 110 | 9x |
x <- tolower(enc2utf8(as.character(x))) |
| 111 | 9x |
x <- gsub("\\s+", " ", trimws(x))
|
| 112 | 9x |
x |
| 113 |
} |
|
| 114 | 3x |
norm_roster <- normalize_name(roster_names) |
| 115 | 3x |
for (i in seq_len(nrow(base))) {
|
| 116 | 8x |
if (is.na(base$preferred_name[i]) || trimws(base$preferred_name[i]) == "") {
|
| 117 | 6x |
nm <- base$transcript_name[i] |
| 118 | 6x |
idx <- which(norm_roster == normalize_name(nm)) |
| 119 | 6x |
if (length(idx) > 0) {
|
| 120 | 3x |
base$preferred_name[i] <- roster_names[idx[1]] |
| 121 | 3x |
base$formal_name[i] <- roster_names[idx[1]] |
| 122 | 3x |
base$participant_type[i] <- "enrolled_student" |
| 123 |
# If roster has student_id, map it |
|
| 124 | 3x |
if ("student_id" %in% names(roster_df)) {
|
| 125 | 3x |
base$student_id[i] <- as.character(roster_df$student_id[idx[1]]) |
| 126 |
} |
|
| 127 |
} |
|
| 128 |
} |
|
| 129 |
} |
|
| 130 |
# Default fill-ins |
|
| 131 | 3x |
base$preferred_name[is.na(base$preferred_name)] <- base$transcript_name[is.na(base$preferred_name)] |
| 132 | 3x |
base$formal_name[is.na(base$formal_name)] <- base$transcript_name[is.na(base$formal_name)] |
| 133 | 3x |
base$participant_type[is.na(base$participant_type)] <- "unknown" |
| 134 | 3x |
base |
| 135 |
} |
|
| 136 | ||
| 137 | 3x |
lookup <- create_lookup(transcript_names, roster_names, lookup_df) |
| 138 | ||
| 139 |
# Join back to transcript_df |
|
| 140 | 3x |
result <- transcript_df |
| 141 |
# Ensure transcript_name column is present for join |
|
| 142 | 3x |
if (!"transcript_name" %in% names(result)) {
|
| 143 | 3x |
result$transcript_name <- result[[name_col]] |
| 144 |
} |
|
| 145 | ||
| 146 |
# Row-wise fill from lookup |
|
| 147 | 3x |
result$clean_name <- NA_character_ |
| 148 | 3x |
result$participant_type <- NA_character_ |
| 149 | 3x |
result$student_id <- NA_character_ |
| 150 | 3x |
result$is_matched <- FALSE |
| 151 | 3x |
for (i in seq_len(nrow(result))) {
|
| 152 | 8x |
nm <- result$transcript_name[i] |
| 153 | 8x |
idx <- which(lookup$transcript_name == nm) |
| 154 | 8x |
if (length(idx) > 0) {
|
| 155 | 8x |
lk <- lookup[idx[1], ] |
| 156 | 8x |
result$clean_name[i] <- if (!is.na(lk$preferred_name)) lk$preferred_name else nm |
| 157 | 8x |
result$participant_type[i] <- if (!is.na(lk$participant_type)) lk$participant_type else "unknown" |
| 158 | 8x |
result$student_id[i] <- if (!is.na(lk$student_id)) lk$student_id else NA_character_ |
| 159 | 8x |
result$is_matched[i] <- !is.na(lk$preferred_name) && lk$preferred_name != nm |
| 160 |
} else {
|
|
| 161 | ! |
result$clean_name[i] <- nm |
| 162 | ! |
result$participant_type[i] <- "unknown" |
| 163 | ! |
result$student_id[i] <- NA_character_ |
| 164 | ! |
result$is_matched[i] <- FALSE |
| 165 |
} |
|
| 166 |
} |
|
| 167 | ||
| 168 |
# Apply privacy defaults to outputs unless 'none' |
|
| 169 | 3x |
if (!identical(privacy_level, "none")) {
|
| 170 | 3x |
result <- ensure_privacy(result, privacy_level = privacy_level) |
| 171 |
} |
|
| 172 | 3x |
result |
| 173 |
} |
| 1 |
#' Ethical Compliance Functions |
|
| 2 |
#' |
|
| 3 |
#' Comprehensive ethical compliance tools for educational data analysis. |
|
| 4 |
#' These functions ensure the package promotes participation equity rather than |
|
| 5 |
#' surveillance and maintains the highest ethical standards for educational research. |
|
| 6 |
#' |
|
| 7 |
#' @name ethical_compliance |
|
| 8 |
#' @keywords internal |
|
| 9 |
NULL |
|
| 10 | ||
| 11 |
#' Validate Ethical Use Compliance |
|
| 12 |
#' |
|
| 13 |
#' Validates that the package is being used for educational purposes that promote |
|
| 14 |
#' participation equity rather than surveillance. This function checks usage patterns |
|
| 15 |
#' and provides guidance for ethical implementation. |
|
| 16 |
#' |
|
| 17 |
#' @param usage_context Context of usage. One of `c("research", "teaching", "assessment", "intervention", "other")`
|
|
| 18 |
#' @param data_scope Scope of data being analyzed. One of `c("individual", "section", "course", "institution", "multi_institution")`
|
|
| 19 |
#' @param purpose_statement Optional statement describing the intended use |
|
| 20 |
#' @param check_consent Whether to check for consent documentation |
|
| 21 |
#' @param check_irb Whether to check for IRB approval documentation |
|
| 22 |
#' |
|
| 23 |
#' @return A list containing ethical validation results with the following elements: |
|
| 24 |
#' - `ethically_compliant`: Logical indicating overall ethical compliance |
|
| 25 |
#' - `risk_level`: Risk level assessment (low, medium, high, critical) |
|
| 26 |
#' - `recommendations`: Character vector of ethical recommendations |
|
| 27 |
#' - `required_documentation`: Required documentation for compliance |
|
| 28 |
#' - `institutional_guidance`: Institution-specific guidance |
|
| 29 |
#' |
|
| 30 |
#' @export |
|
| 31 |
#' @keywords deprecated |
|
| 32 |
#' |
|
| 33 |
#' @examples |
|
| 34 |
#' # Validate ethical use for research |
|
| 35 |
#' validation <- validate_ethical_use( |
|
| 36 |
#' usage_context = "research", |
|
| 37 |
#' data_scope = "section", |
|
| 38 |
#' purpose_statement = "Analyzing participation equity to improve teaching methods" |
|
| 39 |
#' ) |
|
| 40 |
#' print(validation$ethically_compliant) |
|
| 41 |
#' print(validation$recommendations) |
|
| 42 |
validate_ethical_use <- function(usage_context = c("research", "teaching", "assessment", "intervention", "other"),
|
|
| 43 |
data_scope = c("individual", "section", "course", "institution", "multi_institution"),
|
|
| 44 |
purpose_statement = NULL, |
|
| 45 |
check_consent = TRUE, |
|
| 46 |
check_irb = TRUE) {
|
|
| 47 |
# DEPRECATED: This function will be removed in the next version |
|
| 48 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 49 | 15x |
warning("Function 'validate_ethical_use' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 50 | ||
| 51 | 15x |
usage_context <- match.arg(usage_context) |
| 52 | 15x |
data_scope <- match.arg(data_scope) |
| 53 | ||
| 54 |
# Initialize results |
|
| 55 | 15x |
result <- list( |
| 56 | 15x |
ethically_compliant = TRUE, |
| 57 | 15x |
risk_level = "low", |
| 58 | 15x |
recommendations = character(0), |
| 59 | 15x |
required_documentation = character(0), |
| 60 | 15x |
institutional_guidance = character(0) |
| 61 |
) |
|
| 62 | ||
| 63 |
# Risk assessment based on context and scope |
|
| 64 | 15x |
risk_factors <- 0 |
| 65 | ||
| 66 |
# Context-based risk assessment |
|
| 67 | 15x |
if (usage_context == "assessment") {
|
| 68 | 4x |
risk_factors <- risk_factors + 2 |
| 69 | 4x |
result$recommendations <- c( |
| 70 | 4x |
result$recommendations, |
| 71 | 4x |
"Assessment use requires careful consideration of student privacy", |
| 72 | 4x |
"Ensure assessment is formative, not punitive", |
| 73 | 4x |
"Consider alternative participation metrics that don't track individual behavior" |
| 74 |
) |
|
| 75 | 11x |
} else if (usage_context == "other") {
|
| 76 | ! |
risk_factors <- risk_factors + 3 |
| 77 | ! |
result$recommendations <- c( |
| 78 | ! |
result$recommendations, |
| 79 | ! |
"Other usage contexts require additional ethical review", |
| 80 | ! |
"Consider consulting with institutional ethics board", |
| 81 | ! |
"Document specific educational purpose and benefits" |
| 82 |
) |
|
| 83 |
} |
|
| 84 | ||
| 85 |
# Scope-based risk assessment |
|
| 86 | 15x |
if (data_scope == "individual") {
|
| 87 | 3x |
risk_factors <- risk_factors + 2 |
| 88 | 3x |
result$recommendations <- c( |
| 89 | 3x |
result$recommendations, |
| 90 | 3x |
"Individual-level analysis requires explicit consent", |
| 91 | 3x |
"Consider aggregating to section/course level for privacy", |
| 92 | 3x |
"Ensure individual data is not used for punitive purposes" |
| 93 |
) |
|
| 94 | 12x |
} else if (data_scope == "multi_institution") {
|
| 95 | 1x |
risk_factors <- risk_factors + 3 |
| 96 | 1x |
result$recommendations <- c( |
| 97 | 1x |
result$recommendations, |
| 98 | 1x |
"Multi-institution studies require IRB approval", |
| 99 | 1x |
"Ensure data sharing agreements are in place", |
| 100 | 1x |
"Consider institutional review board requirements" |
| 101 |
) |
|
| 102 |
} |
|
| 103 | ||
| 104 |
# Purpose statement analysis |
|
| 105 | 15x |
if (!is.null(purpose_statement)) {
|
| 106 | 6x |
purpose_lower <- tolower(purpose_statement) |
| 107 | ||
| 108 |
# Check for surveillance-related terms |
|
| 109 | 6x |
surveillance_terms <- c("surveillance", "monitoring", "tracking", "spying", "watching")
|
| 110 | 6x |
if (any(sapply(surveillance_terms, function(term) grepl(term, purpose_lower)))) {
|
| 111 | 1x |
risk_factors <- risk_factors + 4 |
| 112 | 1x |
result$ethically_compliant <- FALSE |
| 113 | 1x |
result$recommendations <- c( |
| 114 | 1x |
result$recommendations, |
| 115 | 1x |
"CRITICAL: Purpose statement suggests surveillance use", |
| 116 | 1x |
"This package is designed for participation equity, not surveillance", |
| 117 | 1x |
"Reframe purpose to focus on educational improvement and equity" |
| 118 |
) |
|
| 119 |
} |
|
| 120 | ||
| 121 |
# Check for equity-focused terms |
|
| 122 | 6x |
equity_terms <- c("equity", "participation", "engagement", "improvement", "teaching", "learning")
|
| 123 | 6x |
if (any(sapply(equity_terms, function(term) grepl(term, purpose_lower)))) {
|
| 124 | 3x |
risk_factors <- risk_factors - 1 |
| 125 | 3x |
result$recommendations <- c( |
| 126 | 3x |
result$recommendations, |
| 127 | 3x |
"Good: Purpose focuses on educational improvement and equity" |
| 128 |
) |
|
| 129 |
} |
|
| 130 |
} |
|
| 131 | ||
| 132 |
# Determine risk level |
|
| 133 | 15x |
if (risk_factors >= 6) {
|
| 134 | 1x |
result$risk_level <- "critical" |
| 135 | 1x |
result$ethically_compliant <- FALSE |
| 136 | 14x |
} else if (risk_factors >= 4) {
|
| 137 | 1x |
result$risk_level <- "high" |
| 138 | 13x |
} else if (risk_factors >= 2) {
|
| 139 | 3x |
result$risk_level <- "medium" |
| 140 |
} else {
|
|
| 141 | 10x |
result$risk_level <- "low" |
| 142 |
} |
|
| 143 | ||
| 144 |
# Required documentation based on risk level |
|
| 145 | 15x |
if (result$risk_level %in% c("high", "critical")) {
|
| 146 | 2x |
result$required_documentation <- c( |
| 147 | 2x |
"Institutional Review Board (IRB) approval", |
| 148 | 2x |
"Informed consent documentation", |
| 149 | 2x |
"Data management plan", |
| 150 | 2x |
"Privacy impact assessment" |
| 151 |
) |
|
| 152 | 13x |
} else if (result$risk_level == "medium") {
|
| 153 | 3x |
result$required_documentation <- c( |
| 154 | 3x |
"Institutional ethics review", |
| 155 | 3x |
"Data handling procedures", |
| 156 | 3x |
"Privacy safeguards documentation" |
| 157 |
) |
|
| 158 |
} else {
|
|
| 159 | 10x |
result$required_documentation <- c( |
| 160 | 10x |
"Basic privacy compliance documentation" |
| 161 |
) |
|
| 162 |
} |
|
| 163 | ||
| 164 |
# Institutional guidance |
|
| 165 | 15x |
result$institutional_guidance <- c( |
| 166 | 15x |
"Ensure all data handling complies with institutional policies", |
| 167 | 15x |
"Consult with institutional privacy officer if uncertain", |
| 168 | 15x |
"Document all data processing and analysis procedures", |
| 169 | 15x |
"Regularly review and update privacy safeguards" |
| 170 |
) |
|
| 171 | ||
| 172 |
# Add specific guidance for research context |
|
| 173 | 15x |
if (usage_context == "research" && check_irb) {
|
| 174 | 10x |
result$recommendations <- c( |
| 175 | 10x |
result$recommendations, |
| 176 | 10x |
"Research use requires IRB approval", |
| 177 | 10x |
"Document research protocol and methodology", |
| 178 | 10x |
"Ensure research benefits outweigh privacy risks" |
| 179 |
) |
|
| 180 |
} |
|
| 181 | ||
| 182 |
# Add consent guidance |
|
| 183 | 15x |
if (check_consent) {
|
| 184 | 15x |
result$recommendations <- c( |
| 185 | 15x |
result$recommendations, |
| 186 | 15x |
"Ensure appropriate consent is obtained from participants", |
| 187 | 15x |
"Document consent procedures and timing", |
| 188 | 15x |
"Provide clear information about data use and privacy protection" |
| 189 |
) |
|
| 190 |
} |
|
| 191 | ||
| 192 | 15x |
result |
| 193 |
} |
|
| 194 | ||
| 195 |
#' Create Ethical Use Report |
|
| 196 |
#' |
|
| 197 |
#' Generates a comprehensive ethical use report for institutional review |
|
| 198 |
#' and documentation purposes. |
|
| 199 |
#' |
|
| 200 |
#' @param usage_context Context of usage |
|
| 201 |
#' @param data_scope Scope of data being analyzed |
|
| 202 |
#' @param purpose_statement Purpose statement |
|
| 203 |
#' @param institution_name Name of the institution |
|
| 204 |
#' @param contact_person Contact person for questions |
|
| 205 |
#' @param include_guidance Whether to include detailed guidance |
|
| 206 |
#' |
|
| 207 |
#' @return A character string containing the formatted ethical use report |
|
| 208 |
#' |
|
| 209 |
#' @export |
|
| 210 |
#' @keywords deprecated |
|
| 211 |
#' |
|
| 212 |
#' @examples |
|
| 213 |
#' # Generate ethical use report |
|
| 214 |
#' report <- create_ethical_use_report( |
|
| 215 |
#' usage_context = "research", |
|
| 216 |
#' data_scope = "section", |
|
| 217 |
#' purpose_statement = "Analyzing participation patterns to improve teaching methods", |
|
| 218 |
#' institution_name = "Example University", |
|
| 219 |
#' contact_person = "Dr. Jane Smith" |
|
| 220 |
#' ) |
|
| 221 |
#' cat(report) |
|
| 222 |
create_ethical_use_report <- function(usage_context = NULL, |
|
| 223 |
data_scope = NULL, |
|
| 224 |
purpose_statement = NULL, |
|
| 225 |
institution_name = NULL, |
|
| 226 |
contact_person = NULL, |
|
| 227 |
include_guidance = TRUE) {
|
|
| 228 |
# DEPRECATED: This function will be removed in the next version |
|
| 229 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 230 | 5x |
warning("Function 'create_ethical_use_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 231 | ||
| 232 |
# Validate ethical use |
|
| 233 | 5x |
validation <- validate_ethical_use( |
| 234 | 5x |
usage_context = usage_context, |
| 235 | 5x |
data_scope = data_scope, |
| 236 | 5x |
purpose_statement = purpose_statement |
| 237 |
) |
|
| 238 | ||
| 239 |
# Create report header |
|
| 240 | 5x |
report <- paste0( |
| 241 | 5x |
"ETHICAL USE REPORT\n", |
| 242 | 5x |
"==================\n\n", |
| 243 | 5x |
"Generated: ", format(Sys.time(), "%Y-%m-%d %H:%M:%S"), "\n", |
| 244 | 5x |
"Package: zoomstudentengagement v", utils::packageVersion("zoomstudentengagement"), "\n\n"
|
| 245 |
) |
|
| 246 | ||
| 247 |
# Add institution information |
|
| 248 | 5x |
if (!is.null(institution_name)) {
|
| 249 | 1x |
report <- paste0(report, "Institution: ", institution_name, "\n") |
| 250 |
} |
|
| 251 | 5x |
if (!is.null(contact_person)) {
|
| 252 | 1x |
report <- paste0(report, "Contact: ", contact_person, "\n") |
| 253 |
} |
|
| 254 | 5x |
report <- paste0(report, "\n") |
| 255 | ||
| 256 |
# Add usage information |
|
| 257 | 5x |
report <- paste0( |
| 258 | 5x |
report, |
| 259 | 5x |
"USAGE INFORMATION\n", |
| 260 | 5x |
"-----------------\n", |
| 261 | 5x |
"Context: ", usage_context, "\n", |
| 262 | 5x |
"Scope: ", data_scope, "\n" |
| 263 |
) |
|
| 264 | ||
| 265 | 5x |
if (!is.null(purpose_statement)) {
|
| 266 | 1x |
report <- paste0(report, "Purpose: ", purpose_statement, "\n") |
| 267 |
} |
|
| 268 | 5x |
report <- paste0(report, "\n") |
| 269 | ||
| 270 |
# Add validation results |
|
| 271 | 5x |
report <- paste0( |
| 272 | 5x |
report, |
| 273 | 5x |
"ETHICAL VALIDATION\n", |
| 274 | 5x |
"------------------\n", |
| 275 | 5x |
"Compliant: ", ifelse(validation$ethically_compliant, "YES", "NO"), "\n", |
| 276 | 5x |
"Risk Level: ", toupper(validation$risk_level), "\n\n" |
| 277 |
) |
|
| 278 | ||
| 279 |
# Add recommendations |
|
| 280 | 5x |
if (length(validation$recommendations) > 0) {
|
| 281 | 5x |
report <- paste0( |
| 282 | 5x |
report, |
| 283 | 5x |
"RECOMMENDATIONS\n", |
| 284 | 5x |
"---------------\n" |
| 285 |
) |
|
| 286 | 5x |
for (rec in validation$recommendations) {
|
| 287 | 28x |
report <- paste0(report, "- ", rec, "\n") |
| 288 |
} |
|
| 289 | 5x |
report <- paste0(report, "\n") |
| 290 |
} |
|
| 291 | ||
| 292 |
# Add required documentation |
|
| 293 | 5x |
if (length(validation$required_documentation) > 0) {
|
| 294 | 5x |
report <- paste0( |
| 295 | 5x |
report, |
| 296 | 5x |
"REQUIRED DOCUMENTATION\n", |
| 297 | 5x |
"----------------------\n" |
| 298 |
) |
|
| 299 | 5x |
for (doc in validation$required_documentation) {
|
| 300 | 5x |
report <- paste0(report, "- ", doc, "\n") |
| 301 |
} |
|
| 302 | 5x |
report <- paste0(report, "\n") |
| 303 |
} |
|
| 304 | ||
| 305 |
# Add institutional guidance |
|
| 306 | 5x |
if (include_guidance && length(validation$institutional_guidance) > 0) {
|
| 307 | 5x |
report <- paste0( |
| 308 | 5x |
report, |
| 309 | 5x |
"INSTITUTIONAL GUIDANCE\n", |
| 310 | 5x |
"----------------------\n" |
| 311 |
) |
|
| 312 | 5x |
for (guidance in validation$institutional_guidance) {
|
| 313 | 20x |
report <- paste0(report, "- ", guidance, "\n") |
| 314 |
} |
|
| 315 | 5x |
report <- paste0(report, "\n") |
| 316 |
} |
|
| 317 | ||
| 318 |
# Add ethical principles |
|
| 319 | 5x |
report <- paste0( |
| 320 | 5x |
report, |
| 321 | 5x |
"ETHICAL PRINCIPLES\n", |
| 322 | 5x |
"------------------\n", |
| 323 | 5x |
"This package is designed to promote:\n", |
| 324 | 5x |
"- Participation equity and inclusion\n", |
| 325 | 5x |
"- Educational improvement and learning outcomes\n", |
| 326 | 5x |
"- Privacy protection and data security\n", |
| 327 | 5x |
"- Ethical research practices\n\n", |
| 328 | 5x |
"This package is NOT designed for:\n", |
| 329 | 5x |
"- Student surveillance or monitoring\n", |
| 330 | 5x |
"- Punitive assessment or evaluation\n", |
| 331 | 5x |
"- Individual tracking without consent\n", |
| 332 | 5x |
"- Non-educational purposes\n\n" |
| 333 |
) |
|
| 334 | ||
| 335 |
# Add disclaimer |
|
| 336 | 5x |
report <- paste0( |
| 337 | 5x |
report, |
| 338 | 5x |
"DISCLAIMER\n", |
| 339 | 5x |
"----------\n", |
| 340 | 5x |
"This report is generated automatically and should be reviewed by\n", |
| 341 | 5x |
"appropriate institutional authorities. The package maintainers are\n", |
| 342 | 5x |
"not responsible for misuse of this software. Users must ensure\n", |
| 343 | 5x |
"compliance with all applicable laws, regulations, and institutional\n", |
| 344 | 5x |
"policies.\n" |
| 345 |
) |
|
| 346 | ||
| 347 | 5x |
report |
| 348 |
} |
|
| 349 | ||
| 350 |
#' Audit Ethical Usage Patterns |
|
| 351 |
#' |
|
| 352 |
#' Analyzes usage patterns to detect potential ethical concerns and |
|
| 353 |
#' provides recommendations for improvement. |
|
| 354 |
#' |
|
| 355 |
#' @param function_calls Vector of function names that were called |
|
| 356 |
#' @param data_sizes Vector of data sizes processed |
|
| 357 |
#' @param privacy_settings Vector of privacy settings used |
|
| 358 |
#' @param time_period Time period of analysis (in days) |
|
| 359 |
#' |
|
| 360 |
#' @return A list containing audit results with usage patterns and recommendations |
|
| 361 |
#' |
|
| 362 |
#' @export |
|
| 363 |
#' @keywords deprecated |
|
| 364 |
#' |
|
| 365 |
#' @examples |
|
| 366 |
#' # Audit usage patterns |
|
| 367 |
#' audit <- audit_ethical_usage( |
|
| 368 |
#' function_calls = c("analyze_transcripts", "plot_users", "write_metrics"),
|
|
| 369 |
#' data_sizes = c(100, 150, 200), |
|
| 370 |
#' privacy_settings = c("mask", "mask", "ferpa_strict"),
|
|
| 371 |
#' time_period = 30 |
|
| 372 |
#' ) |
|
| 373 |
audit_ethical_usage <- function(function_calls = NULL, |
|
| 374 |
data_sizes = NULL, |
|
| 375 |
privacy_settings = NULL, |
|
| 376 |
time_period = 30) {
|
|
| 377 |
# DEPRECATED: This function will be removed in the next version |
|
| 378 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 379 | 8x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 380 | ! |
warning("Function 'audit_ethical_usage' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 381 |
} |
|
| 382 | ||
| 383 |
# Initialize results |
|
| 384 | 8x |
result <- list( |
| 385 | 8x |
usage_patterns = list(), |
| 386 | 8x |
ethical_concerns = character(0), |
| 387 | 8x |
recommendations = character(0), |
| 388 | 8x |
compliance_score = 100 |
| 389 |
) |
|
| 390 | ||
| 391 |
# Analyze function usage patterns |
|
| 392 | 8x |
function_counts <- table(function_calls) |
| 393 | 8x |
result$usage_patterns$function_usage <- function_counts |
| 394 | ||
| 395 |
# Check for concerning patterns |
|
| 396 | 8x |
if ("write_metrics" %in% names(function_counts) && function_counts["write_metrics"] > 10) {
|
| 397 | 1x |
result$ethical_concerns <- c( |
| 398 | 1x |
result$ethical_concerns, |
| 399 | 1x |
"High frequency of data export - ensure proper data handling" |
| 400 |
) |
|
| 401 | 1x |
result$compliance_score <- result$compliance_score - 10 |
| 402 |
} |
|
| 403 | ||
| 404 |
# Analyze privacy settings |
|
| 405 | 8x |
privacy_counts <- table(privacy_settings) |
| 406 | 8x |
result$usage_patterns$privacy_settings <- privacy_counts |
| 407 | ||
| 408 | 8x |
if ("none" %in% names(privacy_counts)) {
|
| 409 | 2x |
result$ethical_concerns <- c( |
| 410 | 2x |
result$ethical_concerns, |
| 411 | 2x |
"Privacy disabled in some operations - review necessity" |
| 412 |
) |
|
| 413 | 2x |
result$compliance_score <- result$compliance_score - 20 |
| 414 |
} |
|
| 415 | ||
| 416 |
# Analyze data sizes |
|
| 417 | 8x |
if (length(data_sizes) > 0) {
|
| 418 | 7x |
avg_size <- mean(data_sizes, na.rm = TRUE) |
| 419 | 7x |
max_size <- max(data_sizes, na.rm = TRUE) |
| 420 | ||
| 421 | 7x |
result$usage_patterns$data_sizes <- list( |
| 422 | 7x |
average = avg_size, |
| 423 | 7x |
maximum = max_size, |
| 424 | 7x |
total_operations = length(data_sizes) |
| 425 |
) |
|
| 426 | ||
| 427 | 7x |
if (max_size > 1000) {
|
| 428 | 1x |
result$ethical_concerns <- c( |
| 429 | 1x |
result$ethical_concerns, |
| 430 | 1x |
"Large datasets processed - ensure appropriate consent and safeguards" |
| 431 |
) |
|
| 432 | 1x |
result$compliance_score <- result$compliance_score - 15 |
| 433 |
} |
|
| 434 |
} |
|
| 435 | ||
| 436 |
# Generate recommendations |
|
| 437 | 8x |
if (result$compliance_score < 90) {
|
| 438 | 3x |
result$recommendations <- c( |
| 439 | 3x |
result$recommendations, |
| 440 | 3x |
"Review privacy settings and ensure consistent use of privacy protection", |
| 441 | 3x |
"Consider reducing data export frequency", |
| 442 | 3x |
"Implement additional safeguards for large datasets" |
| 443 |
) |
|
| 444 |
} |
|
| 445 | ||
| 446 | 8x |
if (result$compliance_score >= 90) {
|
| 447 | 5x |
result$recommendations <- c( |
| 448 | 5x |
result$recommendations, |
| 449 | 5x |
"Good ethical usage patterns detected", |
| 450 | 5x |
"Continue to maintain privacy-first approach", |
| 451 | 5x |
"Regularly review usage patterns for compliance" |
| 452 |
) |
|
| 453 |
} |
|
| 454 | ||
| 455 | 8x |
result |
| 456 |
} |
| 1 |
#' Create Session Mapping from Zoom Recordings and Course Information |
|
| 2 |
#' |
|
| 3 |
#' This function creates a mapping between Zoom recordings and course information |
|
| 4 |
#' by matching recording topics with course patterns. |
|
| 5 |
#' |
|
| 6 |
#' @param zoom_recordings_df A tibble containing Zoom recording information with |
|
| 7 |
#' columns: ID, Topic, Start Time |
|
| 8 |
#' @param course_info_df A tibble containing course information created by |
|
| 9 |
#' `create_course_info()` with columns: dept, course, section, instructor, |
|
| 10 |
#' session_length_hours |
|
| 11 |
#' @param output_file Optional file path to save the mapping CSV file |
|
| 12 |
#' @param semester_start_mdy Semester start date in "MMM DD, YYYY" format |
|
| 13 |
#' @param auto_assign_patterns List of patterns for automatic assignment |
|
| 14 |
#' @param interactive Whether to enable interactive assignment for unmatched recordings |
|
| 15 |
#' (prompts only shown in interactive sessions). In non-interactive sessions, |
|
| 16 |
#' a quiet fallback is used. Default is FALSE. |
|
| 17 |
#' @param verbose Logical flag to enable diagnostic output. Defaults to FALSE. |
|
| 18 |
#' |
|
| 19 |
#' @return A tibble with session mapping information |
|
| 20 |
#' @export |
|
| 21 |
#' @keywords deprecated |
|
| 22 |
#' |
|
| 23 |
#' @examples |
|
| 24 |
#' \dontrun{
|
|
| 25 |
#' # Create sample Zoom recordings data |
|
| 26 |
#' zoom_recordings <- tibble::tibble( |
|
| 27 |
#' ID = c("123456789", "987654321"),
|
|
| 28 |
#' Topic = c("CS 101 - Monday 10:00 AM (Dr. Smith)", "MATH 250 - Tuesday 2:00 PM (Dr. Johnson)"),
|
|
| 29 |
#' `Start Time` = c("Jan 15, 2024 10:00 AM", "Jan 16, 2024 2:00 PM")
|
|
| 30 |
#' ) |
|
| 31 |
#' |
|
| 32 |
#' # Create course information |
|
| 33 |
#' course_info <- create_course_info( |
|
| 34 |
#' dept = c("CS", "MATH"),
|
|
| 35 |
#' course = c("101", "250"),
|
|
| 36 |
#' section = c("1", "1"),
|
|
| 37 |
#' instructor = c("Dr. Smith", "Dr. Johnson"),
|
|
| 38 |
#' session_length_hours = c(1.5, 2.0) |
|
| 39 |
#' ) |
|
| 40 |
#' |
|
| 41 |
#' # Create session mapping |
|
| 42 |
#' session_mapping <- create_session_mapping( |
|
| 43 |
#' zoom_recordings_df = zoom_recordings, |
|
| 44 |
#' course_info_df = course_info, |
|
| 45 |
#' output_file = "session_mapping.csv", |
|
| 46 |
#' semester_start_mdy = "Jan 15, 2024" |
|
| 47 |
#' ) |
|
| 48 |
#' } |
|
| 49 |
create_session_mapping <- function( |
|
| 50 |
zoom_recordings_df = NULL, |
|
| 51 |
course_info_df = NULL, |
|
| 52 |
output_file = "session_mapping.csv", |
|
| 53 |
semester_start_mdy = "Jan 01, 2024", |
|
| 54 |
auto_assign_patterns = list( |
|
| 55 |
"CS 101" = "CS.*101", |
|
| 56 |
"MATH 250" = "MATH.*250", |
|
| 57 |
"LTF 201" = "LTF.*201" |
|
| 58 |
), |
|
| 59 |
interactive = FALSE, |
|
| 60 |
verbose = FALSE) {
|
|
| 61 |
# DEPRECATED: This function will be removed in the next version |
|
| 62 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 63 | 30x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 64 | 1x |
warning("Function 'create_session_mapping' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 65 |
} |
|
| 66 | ||
| 67 |
# Declare global variables to avoid R CMD check warnings |
|
| 68 | 30x |
ID <- Topic <- `Start Time` <- start_time <- session_date <- zoom_recording_id <- |
| 69 | 30x |
topic <- session_time <- notes <- NULL |
| 70 | ||
| 71 |
# Input validation |
|
| 72 | 30x |
if (!tibble::is_tibble(zoom_recordings_df)) {
|
| 73 | 1x |
stop("zoom_recordings_df must be a tibble")
|
| 74 |
} |
|
| 75 | 29x |
if (!tibble::is_tibble(course_info_df)) {
|
| 76 | ! |
stop("course_info_df must be a tibble")
|
| 77 |
} |
|
| 78 | ||
| 79 |
# Required columns for course_info_df |
|
| 80 | 29x |
required_cols <- c("dept", "course", "section", "instructor", "session_length_hours")
|
| 81 | 29x |
missing_cols <- setdiff(required_cols, names(course_info_df)) |
| 82 | 29x |
if (length(missing_cols) > 0) {
|
| 83 | 1x |
stop("course_info_df must contain columns: ", paste(missing_cols, collapse = ", "))
|
| 84 |
} |
|
| 85 | ||
| 86 |
# Create base mapping structure using base R instead of dplyr to avoid segmentation fault |
|
| 87 | 28x |
mapping_df <- data.frame( |
| 88 | 28x |
zoom_recording_id = zoom_recordings_df$ID, |
| 89 | 28x |
topic = zoom_recordings_df$Topic, |
| 90 | 28x |
start_time = zoom_recordings_df$`Start Time`, |
| 91 | 28x |
stringsAsFactors = FALSE |
| 92 |
) |
|
| 93 | ||
| 94 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 95 |
# Handle empty data frame case |
|
| 96 | 28x |
if (nrow(mapping_df) == 0) {
|
| 97 | 1x |
mapping_df$dept <- character(0) |
| 98 | 1x |
mapping_df$course <- character(0) |
| 99 | 1x |
mapping_df$section <- character(0) |
| 100 | 1x |
mapping_df$session_date <- as.POSIXct(character(0)) |
| 101 | 1x |
mapping_df$session_time <- character(0) |
| 102 | 1x |
mapping_df$instructor <- character(0) |
| 103 | 1x |
mapping_df$notes <- character(0) |
| 104 |
} else {
|
|
| 105 | 27x |
mapping_df$dept <- NA_character_ |
| 106 | 27x |
mapping_df$course <- NA_character_ |
| 107 | 27x |
mapping_df$section <- NA_character_ |
| 108 | 27x |
mapping_df$session_date <- lubridate::parse_date_time( |
| 109 | 27x |
mapping_df$start_time, |
| 110 | 27x |
orders = c("b d, Y I:M:S p", "b d, Y I:M p", "b d, Y I:M:S", "b d, Y I:M"),
|
| 111 | 27x |
tz = "America/Los_Angeles", |
| 112 | 27x |
quiet = TRUE |
| 113 |
) |
|
| 114 | 27x |
mapping_df$session_time <- format(mapping_df$session_date, "%H:%M") |
| 115 | 27x |
mapping_df$instructor <- NA_character_ |
| 116 | 27x |
mapping_df$notes <- NA_character_ |
| 117 |
} |
|
| 118 | ||
| 119 |
# Attempt automatic assignment based on patterns |
|
| 120 | 28x |
if (length(auto_assign_patterns) > 0) {
|
| 121 | 26x |
for (pattern_name in names(auto_assign_patterns)) {
|
| 122 | 45x |
pattern <- auto_assign_patterns[[pattern_name]] |
| 123 | ||
| 124 |
# Find matching course info using base R instead of dplyr to avoid segmentation fault |
|
| 125 | 45x |
course_dept_course <- paste(course_info_df$dept, course_info_df$course, sep = " ") |
| 126 | 45x |
matching_rows <- stringr::str_detect(course_dept_course, pattern_name) |
| 127 | 45x |
course_match <- course_info_df[matching_rows, , drop = FALSE] |
| 128 | ||
| 129 | 45x |
if (nrow(course_match) > 0) {
|
| 130 |
# Apply pattern to topic matching |
|
| 131 | 33x |
matching_rows <- stringr::str_detect(mapping_df$topic, pattern) |
| 132 | 33x |
mapping_df$dept[matching_rows] <- course_match$dept[1] |
| 133 | 33x |
mapping_df$course[matching_rows] <- course_match$course[1] |
| 134 | 33x |
mapping_df$section[matching_rows] <- course_match$section[1] |
| 135 | 33x |
mapping_df$instructor[matching_rows] <- course_match$instructor[1] |
| 136 |
} |
|
| 137 |
} |
|
| 138 |
} |
|
| 139 | ||
| 140 |
# Interactive assignment (only in interactive sessions when enabled) |
|
| 141 | 28x |
if (isTRUE(interactive) && interactive()) {
|
| 142 |
# Use base R subsetting instead of dplyr::filter to avoid segmentation fault |
|
| 143 | ! |
unmatched_indices <- which(is.na(mapping_df$dept) | is.na(mapping_df$course) | is.na(mapping_df$section)) |
| 144 | ! |
unmatched <- mapping_df[unmatched_indices, , drop = FALSE] |
| 145 | ||
| 146 | ! |
if (nrow(unmatched) > 0) {
|
| 147 | ! |
diag_cat("Found", nrow(unmatched), "unmatched recordings:\n")
|
| 148 | ||
| 149 | ! |
for (i in seq_len(nrow(unmatched))) {
|
| 150 | ! |
recording <- unmatched[i, ] |
| 151 | ! |
diag_cat("\nRecording", i, "of", nrow(unmatched), ":\n")
|
| 152 | ! |
diag_cat("ID:", recording$zoom_recording_id, "\n")
|
| 153 | ! |
diag_cat("Topic:", recording$topic, "\n")
|
| 154 | ! |
diag_cat("Date:", as.character(recording$session_date), "\n")
|
| 155 | ||
| 156 |
# Show available courses |
|
| 157 | ! |
diag_cat("\nAvailable courses:\n")
|
| 158 | ! |
for (j in seq_len(nrow(course_info_df))) {
|
| 159 | ! |
course <- course_info_df[j, ] |
| 160 | ! |
diag_cat(j, ":", course$dept, course$course, "Section", course$section, "\n") |
| 161 |
} |
|
| 162 | ||
| 163 |
# Get user input |
|
| 164 | ! |
diag_cat("\nEnter course number (or 0 to skip): ")
|
| 165 | ! |
course_choice <- as.integer(readline()) |
| 166 | ||
| 167 | ! |
if (course_choice > 0 && course_choice <= nrow(course_info_df)) {
|
| 168 | ! |
selected_course <- course_info_df[course_choice, ] |
| 169 | ! |
mapping_df$dept[mapping_df$zoom_recording_id == recording$zoom_recording_id] <- selected_course$dept |
| 170 | ! |
mapping_df$course[mapping_df$zoom_recording_id == recording$zoom_recording_id] <- selected_course$course |
| 171 | ! |
mapping_df$section[mapping_df$zoom_recording_id == recording$zoom_recording_id] <- selected_course$section |
| 172 | ! |
mapping_df$instructor[mapping_df$zoom_recording_id == recording$zoom_recording_id] <- selected_course$instructor |
| 173 |
} |
|
| 174 |
} |
|
| 175 |
} |
|
| 176 | 28x |
} else if (isTRUE(verbose) || is_verbose()) {
|
| 177 |
# Non-interactive fallback: emit a diagnostic summary if requested |
|
| 178 | 1x |
unmatched_indices <- which(is.na(mapping_df$dept) | is.na(mapping_df$course) | is.na(mapping_df$section)) |
| 179 | 1x |
if (length(unmatched_indices) > 0) {
|
| 180 | 1x |
diag_message(paste0( |
| 181 | 1x |
"Non-interactive mode: ", |
| 182 | 1x |
length(unmatched_indices), |
| 183 | 1x |
" unmatched recordings detected. Skipping prompts." |
| 184 |
)) |
|
| 185 |
} |
|
| 186 |
} |
|
| 187 | ||
| 188 |
# Add notes for unmatched recordings |
|
| 189 | 28x |
unmatched_count <- sum(is.na(mapping_df$dept) | is.na(mapping_df$course) | is.na(mapping_df$section)) |
| 190 | 28x |
if (unmatched_count > 0) {
|
| 191 | 11x |
mapping_df$notes[is.na(mapping_df$dept) | is.na(mapping_df$course) | is.na(mapping_df$section)] <- |
| 192 | 11x |
"NEEDS MANUAL ASSIGNMENT" |
| 193 |
# Only show warnings if not in test environment |
|
| 194 | 11x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 195 | 1x |
warning(unmatched_count, " recordings need manual assignment") |
| 196 |
} |
|
| 197 |
} |
|
| 198 | ||
| 199 |
# Save mapping file |
|
| 200 | 28x |
if (!is.null(output_file)) {
|
| 201 | 24x |
readr::write_csv(mapping_df, output_file) |
| 202 |
} |
|
| 203 | ||
| 204 |
# Return mapping with base R operations instead of dplyr to avoid segmentation fault |
|
| 205 |
# Add course_section column with proper NA handling |
|
| 206 | 27x |
if (all(c("dept", "course", "section") %in% names(mapping_df))) {
|
| 207 |
# Handle NA values properly |
|
| 208 | 27x |
course_section_vals <- rep(NA_character_, nrow(mapping_df)) |
| 209 | 27x |
valid_indices <- !is.na(mapping_df$dept) & !is.na(mapping_df$course) & !is.na(mapping_df$section) |
| 210 | 27x |
course_section_vals[valid_indices] <- paste( |
| 211 | 27x |
mapping_df$dept[valid_indices], |
| 212 | 27x |
mapping_df$course[valid_indices], |
| 213 | 27x |
mapping_df$section[valid_indices], |
| 214 | 27x |
sep = "." |
| 215 |
) |
|
| 216 | 27x |
mapping_df$course_section <- course_section_vals |
| 217 |
} else {
|
|
| 218 | ! |
mapping_df$course_section <- rep(NA_character_, nrow(mapping_df)) |
| 219 |
} |
|
| 220 | ||
| 221 |
# Select and rename columns using base R |
|
| 222 | 27x |
result <- mapping_df[, c( |
| 223 | 27x |
"zoom_recording_id", "topic", "start_time", "dept", "course", "section", |
| 224 | 27x |
"course_section", "session_date", "session_time", "instructor", "notes" |
| 225 |
)] |
|
| 226 | 27x |
names(result)[names(result) == "zoom_recording_id"] <- "recording_id" |
| 227 | ||
| 228 |
# Convert to tibble to maintain expected return type |
|
| 229 | 27x |
result <- tibble::as_tibble(result) |
| 230 | ||
| 231 | 27x |
return(result) |
| 232 |
} |
| 1 |
#' Safe Name Matching Workflow |
|
| 2 |
#' |
|
| 3 |
#' Main workflow function for privacy-first name matching. Implements two-stage |
|
| 4 |
#' processing: Stage 1 (unmasked matching in memory) and Stage 2 (privacy masking |
|
| 5 |
#' for outputs). Provides configuration-driven behavior for unmatched names. |
|
| 6 |
#' |
|
| 7 |
#' @param transcript_file_path Path to transcript file to process |
|
| 8 |
#' @param roster_data Data frame containing roster information |
|
| 9 |
#' @param privacy_level Privacy level for processing. One of |
|
| 10 |
#' `c("ferpa_strict", "ferpa_standard", "mask", "none")`.
|
|
| 11 |
#' Defaults to `getOption("zoomstudentengagement.privacy_level", "mask")`.
|
|
| 12 |
#' @param unmatched_names_action Action to take when unmatched names are found. |
|
| 13 |
#' One of `c("stop", "warn")`. Defaults to `getOption("zoomstudentengagement.unmatched_names_action", "stop")`.
|
|
| 14 |
#' @param data_folder Data folder for saving lookup files |
|
| 15 |
#' @param section_names_lookup_file Name of the lookup file |
|
| 16 |
#' |
|
| 17 |
#' @return Processed data with privacy applied |
|
| 18 |
#' @export |
|
| 19 |
#' |
|
| 20 |
#' @examples |
|
| 21 |
#' \dontrun{
|
|
| 22 |
#' # Default behavior (maximum privacy) |
|
| 23 |
#' result <- safe_name_matching_workflow( |
|
| 24 |
#' transcript_file_path = "transcript.vtt", |
|
| 25 |
#' roster_data = roster_df |
|
| 26 |
#' ) |
|
| 27 |
#' |
|
| 28 |
#' # Opt-in for convenience |
|
| 29 |
#' result <- safe_name_matching_workflow( |
|
| 30 |
#' transcript_file_path = "transcript.vtt", |
|
| 31 |
#' roster_data = roster_df, |
|
| 32 |
#' unmatched_names_action = "warn" |
|
| 33 |
#' ) |
|
| 34 |
#' } |
|
| 35 |
safe_name_matching_workflow <- function(transcript_file_path = NULL, |
|
| 36 |
roster_data = NULL, |
|
| 37 |
privacy_level = getOption( |
|
| 38 |
"zoomstudentengagement.privacy_level", |
|
| 39 |
"mask" |
|
| 40 |
), |
|
| 41 |
unmatched_names_action = getOption( |
|
| 42 |
"zoomstudentengagement.unmatched_names_action", |
|
| 43 |
"stop" |
|
| 44 |
), |
|
| 45 |
data_folder = ".", |
|
| 46 |
section_names_lookup_file = "section_names_lookup.csv") {
|
|
| 47 |
# Validate inputs |
|
| 48 | 40x |
if (!is.character(transcript_file_path) || length(transcript_file_path) != 1) {
|
| 49 | 4x |
stop("transcript_file_path must be a single character string", call. = FALSE)
|
| 50 |
} |
|
| 51 | ||
| 52 | 36x |
if (!file.exists(transcript_file_path)) {
|
| 53 | 3x |
stop("Transcript file not found: ", transcript_file_path, call. = FALSE)
|
| 54 |
} |
|
| 55 | ||
| 56 | 33x |
if (!is.data.frame(roster_data)) {
|
| 57 | 3x |
stop("roster_data must be a data frame", call. = FALSE)
|
| 58 |
} |
|
| 59 | ||
| 60 | 30x |
valid_levels <- c("ferpa_strict", "ferpa_standard", "mask", "none")
|
| 61 | 30x |
if (!privacy_level %in% valid_levels) {
|
| 62 | 3x |
stop("Invalid privacy_level. Must be one of: ",
|
| 63 | 3x |
paste(valid_levels, collapse = ", "), |
| 64 | 3x |
call. = FALSE |
| 65 |
) |
|
| 66 |
} |
|
| 67 | ||
| 68 | 27x |
valid_actions <- c("stop", "warn")
|
| 69 | 27x |
if (!unmatched_names_action %in% valid_actions) {
|
| 70 | 3x |
stop("Invalid unmatched_names_action. Must be one of: ",
|
| 71 | 3x |
paste(valid_actions, collapse = ", "), |
| 72 | 3x |
call. = FALSE |
| 73 |
) |
|
| 74 |
} |
|
| 75 | ||
| 76 |
# defer roster content validation until after basic type checks |
|
| 77 | ||
| 78 | 24x |
if (!is.character(data_folder) || length(data_folder) != 1) {
|
| 79 | 2x |
stop("data_folder must be a single character string", call. = FALSE)
|
| 80 |
} |
|
| 81 | ||
| 82 | 22x |
if (!is.character(section_names_lookup_file) || length(section_names_lookup_file) != 1) {
|
| 83 | 2x |
stop("section_names_lookup_file must be a single character string", call. = FALSE)
|
| 84 |
} |
|
| 85 | ||
| 86 |
# Validate roster has at least one usable name column and non-empty values |
|
| 87 | 20x |
roster_name_columns <- c( |
| 88 | 20x |
"first_last", "preferred_name", "formal_name", "name", "student_name" |
| 89 |
) |
|
| 90 | 20x |
has_roster_name_col <- any(roster_name_columns %in% names(roster_data)) |
| 91 | 20x |
non_empty_roster_names <- character(0) |
| 92 | 20x |
if (has_roster_name_col) {
|
| 93 | 17x |
first_found <- intersect(roster_name_columns, names(roster_data))[1] |
| 94 | 17x |
non_empty_roster_names <- roster_data[[first_found]] |
| 95 | 17x |
non_empty_roster_names <- as.character(non_empty_roster_names) |
| 96 | 17x |
non_empty_roster_names <- non_empty_roster_names[ |
| 97 | 17x |
!is.na(non_empty_roster_names) & nchar(trimws(non_empty_roster_names)) > 0 |
| 98 |
] |
|
| 99 |
} |
|
| 100 | 20x |
if (!has_roster_name_col || length(non_empty_roster_names) == 0) {
|
| 101 | 7x |
stop( |
| 102 | 7x |
paste0( |
| 103 | 7x |
"Roster data appears empty or lacks required name columns.\n", |
| 104 | 7x |
"Provide a roster with at least one of these columns: ", |
| 105 | 7x |
paste(roster_name_columns, collapse = ", "), |
| 106 | 7x |
".\n", |
| 107 | 7x |
"See vignette 'roster-cleaning' and example at ", |
| 108 | 7x |
"system.file('extdata/roster.csv', package = 'zoomstudentengagement').\n",
|
| 109 | 7x |
"Tip: You can construct a minimal roster with your own data using ", |
| 110 | 7x |
"columns like 'first_last' or 'preferred_name'." |
| 111 |
), |
|
| 112 | 7x |
call. = FALSE |
| 113 |
) |
|
| 114 |
} |
|
| 115 | ||
| 116 |
# Enhanced empty roster validation |
|
| 117 | 13x |
if (nrow(roster_data) == 0) {
|
| 118 | ! |
stop( |
| 119 | ! |
"Roster data is empty. Please provide a valid roster with student information.\n", |
| 120 | ! |
"See vignette('roster-cleaning') for guidance on creating a proper roster.",
|
| 121 | ! |
call. = FALSE |
| 122 |
) |
|
| 123 |
} |
|
| 124 | ||
| 125 |
# Stage 1: Load and process with real names in memory (quiet by default) |
|
| 126 | 13x |
diag_message("Stage 1: Loading transcript and performing name matching...")
|
| 127 | ||
| 128 |
# Load transcript (real names in memory only) |
|
| 129 | 13x |
transcript_data <- load_zoom_transcript(transcript_file_path) |
| 130 | ||
| 131 |
# Validate transcript has a usable name column |
|
| 132 | 12x |
transcript_name_columns <- c( |
| 133 | 12x |
"transcript_name", "name", "speaker_name", "participant_name" |
| 134 |
) |
|
| 135 | 12x |
has_transcript_name_col <- any(transcript_name_columns %in% names(transcript_data)) |
| 136 | 12x |
if (!has_transcript_name_col) {
|
| 137 | ! |
stop( |
| 138 | ! |
paste0( |
| 139 | ! |
"Transcript file lacks a usable name column.\n", |
| 140 | ! |
"Expected one of: ", |
| 141 | ! |
paste(transcript_name_columns, collapse = ", "), |
| 142 | ! |
".\n", |
| 143 | ! |
"Please verify the transcript format. Supported formats include ", |
| 144 | ! |
"Zoom VTT and chat exports with participant names." |
| 145 |
), |
|
| 146 | ! |
call. = FALSE |
| 147 |
) |
|
| 148 |
} |
|
| 149 | ||
| 150 |
# Add column existence checks to prevent warnings |
|
| 151 | 12x |
required_columns <- c("user_name", "message", "timestamp")
|
| 152 | 12x |
missing_cols <- setdiff(required_columns, names(transcript_data)) |
| 153 | 12x |
if (length(missing_cols) > 0) {
|
| 154 | 12x |
warning( |
| 155 | 12x |
"Missing columns in transcript data: ", paste(missing_cols, collapse = ", "), "\n", |
| 156 | 12x |
"This may affect processing. Expected columns: ", paste(required_columns, collapse = ", "), |
| 157 | 12x |
call. = FALSE |
| 158 |
) |
|
| 159 |
} |
|
| 160 | ||
| 161 |
# Load existing name mappings |
|
| 162 | 12x |
name_mappings <- tryCatch( |
| 163 |
{
|
|
| 164 | 12x |
load_section_names_lookup( |
| 165 | 12x |
data_folder = data_folder, |
| 166 | 12x |
names_lookup_file = section_names_lookup_file |
| 167 |
) |
|
| 168 |
}, |
|
| 169 | 12x |
error = function(e) {
|
| 170 |
# If no mappings exist, create empty data frame |
|
| 171 | ! |
data.frame( |
| 172 | ! |
transcript_name = character(0), |
| 173 | ! |
preferred_name = character(0), |
| 174 | ! |
formal_name = character(0), |
| 175 | ! |
participant_type = character(0), |
| 176 | ! |
student_id = character(0), |
| 177 | ! |
stringsAsFactors = FALSE |
| 178 |
) |
|
| 179 |
} |
|
| 180 |
) |
|
| 181 | ||
| 182 |
# Detect unmatched names |
|
| 183 | 12x |
unmatched_names <- detect_unmatched_names( |
| 184 | 12x |
transcript_data = transcript_data, |
| 185 | 12x |
roster_data = roster_data, |
| 186 | 12x |
name_mappings = name_mappings, |
| 187 | 12x |
privacy_level = "none" # Need real names for detection |
| 188 |
) |
|
| 189 | ||
| 190 |
# Handle unmatched names according to configuration |
|
| 191 | 12x |
if (length(unmatched_names) > 0) {
|
| 192 | 2x |
handle_unmatched_names( |
| 193 | 2x |
unmatched_names = unmatched_names, |
| 194 | 2x |
unmatched_names_action = unmatched_names_action, |
| 195 | 2x |
privacy_level = privacy_level, |
| 196 | 2x |
data_folder = data_folder, |
| 197 | 2x |
section_names_lookup_file = section_names_lookup_file |
| 198 |
) |
|
| 199 |
} |
|
| 200 | ||
| 201 |
# Stage 2: Apply privacy masking to outputs (quiet by default) |
|
| 202 | 10x |
diag_message("Stage 2: Applying privacy masking to outputs...")
|
| 203 | ||
| 204 |
# Process transcript with privacy-aware matching |
|
| 205 | 10x |
processed_data <- process_transcript_with_privacy( |
| 206 | 10x |
transcript_data = transcript_data, |
| 207 | 10x |
roster_data = roster_data, |
| 208 | 10x |
name_mappings = name_mappings, |
| 209 | 10x |
privacy_level = privacy_level |
| 210 |
) |
|
| 211 | ||
| 212 |
# Validate privacy compliance |
|
| 213 | 10x |
validate_privacy_compliance( |
| 214 | 10x |
data = processed_data, |
| 215 | 10x |
privacy_level = privacy_level, |
| 216 | 10x |
real_names = c( |
| 217 | 10x |
extract_transcript_names(transcript_data), |
| 218 | 10x |
extract_roster_names(roster_data) |
| 219 |
) |
|
| 220 |
) |
|
| 221 | ||
| 222 |
# Explicitly clear real names from memory |
|
| 223 | 10x |
rm(transcript_data, name_mappings, unmatched_names) |
| 224 | ||
| 225 | 10x |
diag_message("Name matching workflow completed successfully.")
|
| 226 | ||
| 227 |
# Return processed data |
|
| 228 | 10x |
processed_data |
| 229 |
} |
|
| 230 | ||
| 231 |
#' Handle Unmatched Names |
|
| 232 |
#' |
|
| 233 |
#' Internal function to handle unmatched names according to configuration. |
|
| 234 |
#' |
|
| 235 |
#' @param unmatched_names Character vector of unmatched names |
|
| 236 |
#' @param unmatched_names_action Action to take ("stop" or "warn")
|
|
| 237 |
#' @param privacy_level Privacy level for the session |
|
| 238 |
#' @param data_folder Data folder path |
|
| 239 |
#' @param section_names_lookup_file Name of the lookup file |
|
| 240 |
#' |
|
| 241 |
#' @return Invisibly returns NULL |
|
| 242 |
#' @keywords internal |
|
| 243 |
handle_unmatched_names <- function(unmatched_names, |
|
| 244 |
unmatched_names_action, |
|
| 245 |
privacy_level, |
|
| 246 |
data_folder, |
|
| 247 |
section_names_lookup_file) {
|
|
| 248 |
# DEPRECATED: This function will be removed in the next version |
|
| 249 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 250 | 6x |
warning("Function 'handle_unmatched_names' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 251 | ||
| 252 | 6x |
if (identical(unmatched_names_action, "stop")) {
|
| 253 |
# Stop with error for maximum privacy protection |
|
| 254 | 3x |
stop( |
| 255 | 3x |
paste0( |
| 256 | 3x |
"Found unmatched names: ", paste(unmatched_names, collapse = ", "), "\n", |
| 257 | 3x |
"Please update your section_names_lookup.csv file with these mappings.\n", |
| 258 | 3x |
"See vignette('name-matching-troubleshooting') for detailed instructions.\n",
|
| 259 | 3x |
"Example mappings:\n", |
| 260 | 3x |
paste(sapply(unmatched_names, function(name) {
|
| 261 | 5x |
paste0(" ", name, " -> [Your roster name]")
|
| 262 | 3x |
}), collapse = "\n"), "\n", |
| 263 | 3x |
"Lookup file path: ", file.path(data_folder, section_names_lookup_file), "\n", |
| 264 | 3x |
"For guided assistance, set unmatched_names_action = 'warn' to receive a template." |
| 265 |
), |
|
| 266 | 3x |
call. = FALSE |
| 267 |
) |
|
| 268 | 3x |
} else if (identical(unmatched_names_action, "warn")) {
|
| 269 |
# Show warning and prompt user for matching |
|
| 270 | 3x |
warning( |
| 271 | 3x |
"Some names need matching. Privacy temporarily disabled for matching process.", |
| 272 | 3x |
call. = FALSE |
| 273 |
) |
|
| 274 | ||
| 275 |
# Prompt user for name matching |
|
| 276 | 3x |
prompt_name_matching( |
| 277 | 3x |
unmatched_names = unmatched_names, |
| 278 | 3x |
privacy_level = privacy_level, |
| 279 | 3x |
data_folder = data_folder, |
| 280 | 3x |
section_names_lookup_file = section_names_lookup_file |
| 281 |
) |
|
| 282 | ||
| 283 |
# Stop processing to allow user to update mappings |
|
| 284 | 3x |
stop( |
| 285 | 3x |
"Please update the name mappings file and re-run the analysis.", |
| 286 | 3x |
call. = FALSE |
| 287 |
) |
|
| 288 |
} |
|
| 289 |
} |
|
| 290 | ||
| 291 |
#' Process Transcript with Privacy |
|
| 292 |
#' |
|
| 293 |
#' Processes transcript data with privacy-aware name matching. This function |
|
| 294 |
#' implements the two-stage approach: matching with real names in memory, |
|
| 295 |
#' then applying privacy masking to outputs. |
|
| 296 |
#' |
|
| 297 |
#' @param transcript_data Data frame containing transcript data |
|
| 298 |
#' @param roster_data Data frame containing roster data |
|
| 299 |
#' @param name_mappings Data frame containing name mappings |
|
| 300 |
#' @param privacy_level Privacy level for processing |
|
| 301 |
#' |
|
| 302 |
#' @return Processed data with privacy applied |
|
| 303 |
#' @export |
|
| 304 |
#' |
|
| 305 |
#' @examples |
|
| 306 |
#' # Process transcript with privacy |
|
| 307 |
#' transcript_data <- tibble::tibble( |
|
| 308 |
#' transcript_name = c("Dr. Smith", "John Doe"),
|
|
| 309 |
#' message = c("Hello class", "Good morning")
|
|
| 310 |
#' ) |
|
| 311 |
#' roster_data <- tibble::tibble( |
|
| 312 |
#' first_name = c("John"),
|
|
| 313 |
#' last_name = c("Doe")
|
|
| 314 |
#' ) |
|
| 315 |
#' processed <- process_transcript_with_privacy( |
|
| 316 |
#' transcript_data = transcript_data, |
|
| 317 |
#' roster_data = roster_data |
|
| 318 |
#' ) |
|
| 319 |
process_transcript_with_privacy <- function(transcript_data = NULL, |
|
| 320 |
roster_data = NULL, |
|
| 321 |
name_mappings = NULL, |
|
| 322 |
privacy_level = getOption( |
|
| 323 |
"zoomstudentengagement.privacy_level", |
|
| 324 |
"mask" |
|
| 325 |
)) {
|
|
| 326 |
# DEPRECATED: This function will be removed in the next version |
|
| 327 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 328 | 19x |
warning("Function 'process_transcript_with_privacy' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 329 | ||
| 330 |
# Validate inputs |
|
| 331 | 19x |
if (!is.data.frame(transcript_data)) {
|
| 332 | 1x |
stop("transcript_data must be a data frame", call. = FALSE)
|
| 333 |
} |
|
| 334 | 18x |
if (!is.data.frame(roster_data)) {
|
| 335 | 1x |
stop("roster_data must be a data frame", call. = FALSE)
|
| 336 |
} |
|
| 337 | ||
| 338 | 17x |
valid_levels <- c("ferpa_strict", "ferpa_standard", "mask", "none")
|
| 339 | 17x |
if (!privacy_level %in% valid_levels) {
|
| 340 | 2x |
stop("Invalid privacy_level. Must be one of: ",
|
| 341 | 2x |
paste(valid_levels, collapse = ", "), |
| 342 | 2x |
call. = FALSE |
| 343 |
) |
|
| 344 |
} |
|
| 345 | ||
| 346 |
# Ensure transcript has a usable name column before proceeding |
|
| 347 | 15x |
transcript_name_columns <- c( |
| 348 | 15x |
"transcript_name", "name", "speaker_name", "participant_name" |
| 349 |
) |
|
| 350 | 15x |
if (!any(transcript_name_columns %in% names(transcript_data))) {
|
| 351 | 3x |
stop("No name column found in transcript data", call. = FALSE)
|
| 352 |
} |
|
| 353 | ||
| 354 |
# Stage 1: Perform name matching with real names in memory |
|
| 355 | 12x |
matched_data <- match_names_with_privacy( |
| 356 | 12x |
transcript_data = transcript_data, |
| 357 | 12x |
roster_data = roster_data, |
| 358 | 12x |
name_mappings = name_mappings, |
| 359 | 12x |
privacy_level = "none" # Need real names for matching |
| 360 |
) |
|
| 361 | ||
| 362 |
# Stage 2: Apply privacy masking to outputs |
|
| 363 | 12x |
if (!identical(privacy_level, "none")) {
|
| 364 | 6x |
matched_data <- ensure_privacy(matched_data, privacy_level = privacy_level) |
| 365 |
} |
|
| 366 | ||
| 367 |
# Return processed data |
|
| 368 | 12x |
matched_data |
| 369 |
} |
|
| 370 | ||
| 371 |
#' Match Names with Privacy |
|
| 372 |
#' |
|
| 373 |
#' Performs comprehensive name matching with privacy awareness. Uses consistent |
|
| 374 |
#' hashing for cross-session matching while maintaining privacy controls. |
|
| 375 |
#' |
|
| 376 |
#' @param transcript_data Data frame containing transcript data |
|
| 377 |
#' @param roster_data Data frame containing roster data |
|
| 378 |
#' @param name_mappings Data frame containing name mappings |
|
| 379 |
#' @param privacy_level Privacy level for processing |
|
| 380 |
#' |
|
| 381 |
#' @return Matched data with privacy controls applied |
|
| 382 |
#' @export |
|
| 383 |
#' |
|
| 384 |
#' @examples |
|
| 385 |
#' # Match names with privacy |
|
| 386 |
#' transcript_data <- tibble::tibble( |
|
| 387 |
#' transcript_name = c("Dr. Smith", "John Doe"),
|
|
| 388 |
#' message = c("Hello class", "Good morning")
|
|
| 389 |
#' ) |
|
| 390 |
#' roster_data <- tibble::tibble( |
|
| 391 |
#' first_name = c("John"),
|
|
| 392 |
#' last_name = c("Doe")
|
|
| 393 |
#' ) |
|
| 394 |
#' matched <- match_names_with_privacy( |
|
| 395 |
#' transcript_data = transcript_data, |
|
| 396 |
#' roster_data = roster_data |
|
| 397 |
#' ) |
|
| 398 |
match_names_with_privacy <- function(transcript_data = NULL, |
|
| 399 |
roster_data = NULL, |
|
| 400 |
name_mappings = NULL, |
|
| 401 |
privacy_level = getOption( |
|
| 402 |
"zoomstudentengagement.privacy_level", |
|
| 403 |
"mask" |
|
| 404 |
)) {
|
|
| 405 |
# DEPRECATED: This function will be removed in the next version |
|
| 406 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 407 | 19x |
warning("Function 'match_names_with_privacy' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 408 | ||
| 409 |
# Validate inputs |
|
| 410 | 19x |
if (!is.data.frame(transcript_data)) {
|
| 411 | 1x |
stop("transcript_data must be a data frame", call. = FALSE)
|
| 412 |
} |
|
| 413 | 18x |
if (!is.data.frame(roster_data)) {
|
| 414 | 1x |
stop("roster_data must be a data frame", call. = FALSE)
|
| 415 |
} |
|
| 416 | ||
| 417 | 17x |
valid_levels <- c("ferpa_strict", "ferpa_standard", "mask", "none")
|
| 418 | 17x |
if (!privacy_level %in% valid_levels) {
|
| 419 | 2x |
stop("Invalid privacy_level. Must be one of: ",
|
| 420 | 2x |
paste(valid_levels, collapse = ", "), |
| 421 | 2x |
call. = FALSE |
| 422 |
) |
|
| 423 |
} |
|
| 424 | ||
| 425 |
# Extract names for matching |
|
| 426 | 15x |
transcript_names <- extract_transcript_names(transcript_data) |
| 427 |
# If we couldn't extract any names, fail early with clear message |
|
| 428 | 15x |
if (length(transcript_names) == 0) {
|
| 429 | 1x |
stop("No name column found in transcript data", call. = FALSE)
|
| 430 |
} |
|
| 431 | 14x |
roster_names <- extract_roster_names(roster_data) |
| 432 | ||
| 433 |
# Create name mapping lookup |
|
| 434 | 14x |
name_lookup <- create_name_lookup( |
| 435 | 14x |
transcript_names = transcript_names, |
| 436 | 14x |
roster_names = roster_names, |
| 437 | 14x |
name_mappings = name_mappings |
| 438 |
) |
|
| 439 | ||
| 440 |
# Apply name matching to transcript data |
|
| 441 | 14x |
matched_data <- apply_name_matching( |
| 442 | 14x |
transcript_data = transcript_data, |
| 443 | 14x |
name_lookup = name_lookup, |
| 444 | 14x |
roster_data = roster_data |
| 445 |
) |
|
| 446 | ||
| 447 |
# Apply privacy masking if needed |
|
| 448 | 14x |
if (!identical(privacy_level, "none")) {
|
| 449 | 1x |
matched_data <- ensure_privacy(matched_data, privacy_level = privacy_level) |
| 450 |
} |
|
| 451 | ||
| 452 |
# Return matched data |
|
| 453 | 14x |
matched_data |
| 454 |
} |
|
| 455 | ||
| 456 |
#' Create Name Lookup |
|
| 457 |
#' |
|
| 458 |
#' Internal function to create a lookup table for name matching. |
|
| 459 |
#' |
|
| 460 |
#' @param transcript_names Character vector of transcript names |
|
| 461 |
#' @param roster_names Character vector of roster names |
|
| 462 |
#' @param name_mappings Data frame containing name mappings |
|
| 463 |
#' |
|
| 464 |
#' @return Data frame with name lookup information |
|
| 465 |
#' @keywords internal |
|
| 466 |
create_name_lookup <- function(transcript_names, roster_names, name_mappings) {
|
|
| 467 |
# DEPRECATED: This function will be removed in the next version |
|
| 468 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 469 | 18x |
warning("Function 'create_name_lookup' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 470 | ||
| 471 |
# Handle empty transcript names gracefully |
|
| 472 | 18x |
if (length(transcript_names) == 0) {
|
| 473 | 1x |
return(data.frame( |
| 474 | 1x |
transcript_name = character(0), |
| 475 | 1x |
preferred_name = character(0), |
| 476 | 1x |
formal_name = character(0), |
| 477 | 1x |
participant_type = character(0), |
| 478 | 1x |
student_id = character(0), |
| 479 | 1x |
stringsAsFactors = FALSE |
| 480 |
)) |
|
| 481 |
} |
|
| 482 |
# Start with transcript names |
|
| 483 | 17x |
lookup <- data.frame( |
| 484 | 17x |
transcript_name = transcript_names, |
| 485 | 17x |
preferred_name = NA_character_, |
| 486 | 17x |
formal_name = NA_character_, |
| 487 | 17x |
participant_type = NA_character_, |
| 488 | 17x |
student_id = NA_character_, |
| 489 | 17x |
stringsAsFactors = FALSE |
| 490 |
) |
|
| 491 | ||
| 492 |
# Apply existing mappings |
|
| 493 | 17x |
if (!is.null(name_mappings) && nrow(name_mappings) > 0) {
|
| 494 | 2x |
for (i in seq_len(nrow(lookup))) {
|
| 495 | 4x |
transcript_name <- lookup$transcript_name[i] |
| 496 | ||
| 497 |
# Find matching mapping |
|
| 498 | 4x |
mapping_idx <- which(name_mappings$transcript_name == transcript_name) |
| 499 | 4x |
if (length(mapping_idx) > 0) {
|
| 500 | 2x |
mapping <- name_mappings[mapping_idx[1], ] |
| 501 | 2x |
lookup$preferred_name[i] <- mapping$preferred_name |
| 502 | 2x |
lookup$formal_name[i] <- mapping$formal_name |
| 503 | 2x |
lookup$participant_type[i] <- mapping$participant_type |
| 504 | 2x |
lookup$student_id[i] <- mapping$student_id |
| 505 |
} |
|
| 506 |
} |
|
| 507 |
} |
|
| 508 | ||
| 509 |
# Apply roster matching for unmatched names |
|
| 510 | 17x |
for (i in seq_len(nrow(lookup))) {
|
| 511 | 43x |
if (is.na(lookup$preferred_name[i])) {
|
| 512 | 41x |
transcript_name <- lookup$transcript_name[i] |
| 513 | ||
| 514 |
# Try to match with roster names |
|
| 515 | 41x |
roster_match <- find_roster_match(transcript_name, roster_names) |
| 516 | 41x |
if (!is.null(roster_match)) {
|
| 517 | 34x |
lookup$preferred_name[i] <- roster_match$preferred_name |
| 518 | 34x |
lookup$formal_name[i] <- roster_match$formal_name |
| 519 | 34x |
lookup$participant_type[i] <- "enrolled_student" |
| 520 | 34x |
lookup$student_id[i] <- roster_match$student_id |
| 521 |
} |
|
| 522 |
} |
|
| 523 |
} |
|
| 524 | ||
| 525 |
# Fill in missing values |
|
| 526 | 17x |
lookup$preferred_name[is.na(lookup$preferred_name)] <- lookup$transcript_name[is.na(lookup$preferred_name)] |
| 527 | 17x |
lookup$formal_name[is.na(lookup$formal_name)] <- lookup$transcript_name[is.na(lookup$formal_name)] |
| 528 | 17x |
lookup$participant_type[is.na(lookup$participant_type)] <- "unknown" |
| 529 | ||
| 530 | 17x |
lookup |
| 531 |
} |
|
| 532 | ||
| 533 |
#' Find Roster Match |
|
| 534 |
#' |
|
| 535 |
#' Internal function to find a matching name in the roster. |
|
| 536 |
#' |
|
| 537 |
#' @param transcript_name Character string of transcript name |
|
| 538 |
#' @param roster_names Character vector of roster names |
|
| 539 |
#' |
|
| 540 |
#' @return List with match information or NULL if no match |
|
| 541 |
#' @keywords internal |
|
| 542 |
find_roster_match <- function(transcript_name, roster_names) {
|
|
| 543 |
# DEPRECATED: This function will be removed in the next version |
|
| 544 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 545 | 49x |
warning("Function 'find_roster_match' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 546 | ||
| 547 |
# Normalize names for comparison |
|
| 548 | 49x |
normalized_transcript <- normalize_name_for_matching(transcript_name) |
| 549 | 49x |
normalized_roster <- normalize_name_for_matching(roster_names) |
| 550 | ||
| 551 |
# Find exact matches |
|
| 552 | 49x |
matches <- which(normalized_roster == normalized_transcript) |
| 553 | ||
| 554 | 49x |
if (length(matches) > 0) {
|
| 555 |
# Return first match |
|
| 556 | 39x |
return(list( |
| 557 | 39x |
preferred_name = roster_names[matches[1]], |
| 558 | 39x |
formal_name = roster_names[matches[1]], |
| 559 | 39x |
student_id = NA_character_ |
| 560 |
)) |
|
| 561 |
} |
|
| 562 | ||
| 563 |
# No match found |
|
| 564 | 10x |
NULL |
| 565 |
} |
|
| 566 | ||
| 567 |
#' Apply Name Matching |
|
| 568 |
#' |
|
| 569 |
#' Internal function to apply name matching to transcript data. |
|
| 570 |
#' |
|
| 571 |
#' @param transcript_data Data frame containing transcript data |
|
| 572 |
#' @param name_lookup Data frame with name lookup information |
|
| 573 |
#' @param roster_data Data frame containing roster data |
|
| 574 |
#' |
|
| 575 |
#' @return Data frame with matched names |
|
| 576 |
#' @keywords internal |
|
| 577 |
apply_name_matching <- function(transcript_data, name_lookup, roster_data) {
|
|
| 578 |
# DEPRECATED: This function will be removed in the next version |
|
| 579 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 580 | 18x |
warning("Function 'apply_name_matching' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 581 | ||
| 582 |
# Create a copy of transcript data |
|
| 583 | 18x |
result <- transcript_data |
| 584 | ||
| 585 |
# Add name columns if they don't exist |
|
| 586 | 18x |
if (!"transcript_name" %in% names(result)) {
|
| 587 |
# Look for existing name column |
|
| 588 | 13x |
name_cols <- c("name", "speaker_name", "participant_name")
|
| 589 | 13x |
found_cols <- intersect(name_cols, names(result)) |
| 590 | ||
| 591 | 13x |
if (length(found_cols) > 0) {
|
| 592 | 11x |
result$transcript_name <- result[[found_cols[1]]] |
| 593 |
} else {
|
|
| 594 | 2x |
stop("No name column found in transcript data", call. = FALSE)
|
| 595 |
} |
|
| 596 |
} |
|
| 597 | ||
| 598 |
# Ensure lookup has required columns with proper initialization |
|
| 599 | 16x |
if (!"preferred_name" %in% names(name_lookup)) {
|
| 600 | ! |
name_lookup$preferred_name <- ifelse(is.na(name_lookup$transcript_name), NA_character_, name_lookup$transcript_name) |
| 601 |
} |
|
| 602 | 16x |
if (!"formal_name" %in% names(name_lookup)) {
|
| 603 | ! |
name_lookup$formal_name <- ifelse(is.na(name_lookup$transcript_name), NA_character_, name_lookup$transcript_name) |
| 604 |
} |
|
| 605 | 16x |
if (!"participant_type" %in% names(name_lookup)) {
|
| 606 | ! |
name_lookup$participant_type <- rep("unknown", nrow(name_lookup))
|
| 607 |
} |
|
| 608 | 16x |
if (!"student_id" %in% names(name_lookup)) {
|
| 609 | ! |
name_lookup$student_id <- rep(NA_character_, nrow(name_lookup)) |
| 610 |
} |
|
| 611 | ||
| 612 |
# Ensure result has required columns before the loop |
|
| 613 | 16x |
if (!"preferred_name" %in% names(result)) {
|
| 614 | 16x |
result$preferred_name <- rep(NA_character_, nrow(result)) |
| 615 |
} |
|
| 616 | 16x |
if (!"formal_name" %in% names(result)) {
|
| 617 | 16x |
result$formal_name <- rep(NA_character_, nrow(result)) |
| 618 |
} |
|
| 619 | 16x |
if (!"participant_type" %in% names(result)) {
|
| 620 | 16x |
result$participant_type <- rep("unknown", nrow(result))
|
| 621 |
} |
|
| 622 | 16x |
if (!"student_id" %in% names(result)) {
|
| 623 | 16x |
result$student_id <- rep(NA_character_, nrow(result)) |
| 624 |
} |
|
| 625 | ||
| 626 |
# Apply name matching |
|
| 627 | 16x |
for (i in seq_len(nrow(result))) {
|
| 628 | 39x |
transcript_name <- result$transcript_name[i] |
| 629 | ||
| 630 |
# Find matching lookup entry |
|
| 631 | 39x |
lookup_idx <- which(name_lookup$transcript_name == transcript_name) |
| 632 | 39x |
if (length(lookup_idx) > 0) {
|
| 633 | 39x |
lookup <- name_lookup[lookup_idx[1], ] |
| 634 | ||
| 635 |
# Assign the values |
|
| 636 | 39x |
result$preferred_name[i] <- lookup$preferred_name |
| 637 | 39x |
result$formal_name[i] <- lookup$formal_name |
| 638 | 39x |
result$participant_type[i] <- lookup$participant_type |
| 639 | 39x |
result$student_id[i] <- lookup$student_id |
| 640 |
} |
|
| 641 |
} |
|
| 642 | ||
| 643 |
# Ensure all required columns exist |
|
| 644 | 16x |
if (!"preferred_name" %in% names(result)) {
|
| 645 | ! |
result$preferred_name <- result$transcript_name |
| 646 |
} |
|
| 647 | 16x |
if (!"formal_name" %in% names(result)) {
|
| 648 | ! |
result$formal_name <- result$transcript_name |
| 649 |
} |
|
| 650 | 16x |
if (!"participant_type" %in% names(result)) {
|
| 651 | ! |
result$participant_type <- "unknown" |
| 652 |
} |
|
| 653 | 16x |
if (!"student_id" %in% names(result)) {
|
| 654 | ! |
result$student_id <- NA_character_ |
| 655 |
} |
|
| 656 | ||
| 657 |
# Return result |
|
| 658 | 16x |
result |
| 659 |
} |
| 1 |
#' Lookup Merge Utilities (Safe, Transactional, UTF-8) |
|
| 2 |
#' |
|
| 3 |
#' Utilities to safely read, merge, and write the participant lookup |
|
| 4 |
#' configuration (`section_names_lookup.csv`). All operations are |
|
| 5 |
#' read-then-merge in memory; writes are opt-in and transactional with |
|
| 6 |
#' timestamped backups to prevent accidental data loss. |
|
| 7 |
#' |
|
| 8 |
#' Expected columns in lookup data frame: |
|
| 9 |
#' - transcript_name |
|
| 10 |
#' - preferred_name |
|
| 11 |
#' - formal_name |
|
| 12 |
#' - participant_type (e.g., instructor, enrolled_student, guest, unknown) |
|
| 13 |
#' - student_id |
|
| 14 |
#' - notes (optional) |
|
| 15 |
#' |
|
| 16 |
#' @name lookup_merge_utils |
|
| 17 |
#' @family lookup_utils |
|
| 18 |
NULL |
|
| 19 | ||
| 20 |
.lookup_expected_columns <- function() {
|
|
| 21 | 18x |
c( |
| 22 | 18x |
"transcript_name", |
| 23 | 18x |
"preferred_name", |
| 24 | 18x |
"formal_name", |
| 25 | 18x |
"participant_type", |
| 26 | 18x |
"student_id", |
| 27 | 18x |
"notes" |
| 28 |
) |
|
| 29 |
} |
|
| 30 | ||
| 31 |
.coerce_to_utf8 <- function(x) {
|
|
| 32 | 108x |
if (is.null(x)) {
|
| 33 | ! |
return(x) |
| 34 |
} |
|
| 35 | ! |
if (is.factor(x)) x <- as.character(x) |
| 36 | 108x |
if (!is.character(x)) {
|
| 37 | 1x |
return(x) |
| 38 |
} |
|
| 39 | 107x |
enc2utf8(x) |
| 40 |
} |
|
| 41 | ||
| 42 |
.normalize_lookup_df <- function(df) {
|
|
| 43 | 18x |
if (is.null(df)) {
|
| 44 | ! |
df <- data.frame(stringsAsFactors = FALSE) |
| 45 |
} |
|
| 46 |
# Ensure UTF-8 and expected columns exist |
|
| 47 | 18x |
expected <- .lookup_expected_columns() |
| 48 | 18x |
for (col in expected) {
|
| 49 | 108x |
if (!col %in% names(df)) {
|
| 50 | 6x |
df[[col]] <- rep(NA_character_, nrow(df)) |
| 51 |
} |
|
| 52 |
} |
|
| 53 |
# Subset and order columns deterministically |
|
| 54 | 18x |
df <- df[expected] |
| 55 |
# Coerce character columns to UTF-8 |
|
| 56 | 18x |
for (nm in names(df)) {
|
| 57 | 108x |
df[[nm]] <- .coerce_to_utf8(df[[nm]]) |
| 58 |
} |
|
| 59 |
# Replace NA student_id for instructors with constant |
|
| 60 | 18x |
is_instructor <- !is.na(df$participant_type) & df$participant_type == "instructor" |
| 61 | 18x |
df$student_id[is_instructor & is.na(df$student_id)] <- "INSTRUCTOR" |
| 62 |
# Ensure participant_type known set |
|
| 63 | 18x |
df$participant_type[is.na(df$participant_type)] <- "unknown" |
| 64 |
# Remove completely empty rows (no transcript_name and no preferred_name) |
|
| 65 | 18x |
empty_rows <- (is.na(df$transcript_name) | trimws(df$transcript_name) == "") & |
| 66 | 18x |
(is.na(df$preferred_name) | trimws(df$preferred_name) == "") |
| 67 | 18x |
if (length(empty_rows) > 0 && any(empty_rows)) {
|
| 68 | ! |
df <- df[!empty_rows, , drop = FALSE] |
| 69 |
} |
|
| 70 | 18x |
df |
| 71 |
} |
|
| 72 | ||
| 73 |
#' Read Lookup Safely |
|
| 74 |
#' |
|
| 75 |
#' Reads a lookup CSV safely: normalizes columns, coerces to UTF-8, and |
|
| 76 |
#' returns an empty (0-row) normalized data frame if the file is missing. |
|
| 77 |
#' |
|
| 78 |
#' @param path Character. Path to the lookup CSV file. |
|
| 79 |
#' |
|
| 80 |
#' @return A normalized data frame with expected columns. |
|
| 81 |
#' @export |
|
| 82 |
read_lookup_safely <- function(path = NULL) {
|
|
| 83 |
# DEPRECATED: This function will be removed in the next version |
|
| 84 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 85 | 2x |
warning("Function 'read_lookup_safely' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 86 | ||
| 87 | 2x |
if (!is.character(path) || length(path) != 1) {
|
| 88 | ! |
stop("path must be a single character string", call. = FALSE)
|
| 89 |
} |
|
| 90 | 2x |
if (!file.exists(path)) {
|
| 91 | 1x |
return(.normalize_lookup_df(data.frame(stringsAsFactors = FALSE))) |
| 92 |
} |
|
| 93 | 1x |
df <- utils::read.csv(path, stringsAsFactors = FALSE, fileEncoding = "UTF-8-BOM") |
| 94 | 1x |
.normalize_lookup_df(df) |
| 95 |
} |
|
| 96 | ||
| 97 |
#' Merge Lookup Preserving Existing Rows |
|
| 98 |
#' |
|
| 99 |
#' Merges an additional lookup data frame into an existing one while |
|
| 100 |
#' preserving existing rows. New information fills only missing fields. |
|
| 101 |
#' Duplicates are removed deterministically by `transcript_name` keeping |
|
| 102 |
#' the first non-empty values per field. |
|
| 103 |
#' |
|
| 104 |
#' @param existing_df Data frame. Existing normalized lookup. |
|
| 105 |
#' @param add_df Data frame. Additional rows to merge (will be normalized). |
|
| 106 |
#' |
|
| 107 |
#' @return A merged, normalized, de-duplicated data frame. |
|
| 108 |
#' @export |
|
| 109 |
merge_lookup_preserve <- function(existing_df = NULL, add_df = NULL) {
|
|
| 110 |
# DEPRECATED: This function will be removed in the next version |
|
| 111 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 112 | 3x |
warning("Function 'merge_lookup_preserve' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 113 | ||
| 114 | 3x |
base <- .normalize_lookup_df(existing_df) |
| 115 | 3x |
add <- .normalize_lookup_df(add_df) |
| 116 | ||
| 117 | 3x |
combined <- rbind(base, add) |
| 118 |
# Deterministic ordering by transcript_name then participant_type |
|
| 119 | 3x |
ord <- order(tolower(ifelse(is.na(combined$transcript_name), "", combined$transcript_name)), |
| 120 | 3x |
combined$participant_type, |
| 121 | 3x |
na.last = TRUE |
| 122 |
) |
|
| 123 | 3x |
combined <- combined[ord, , drop = FALSE] |
| 124 | ||
| 125 |
# Deduplicate by transcript_name: keep first non-empty values per column |
|
| 126 | 3x |
if (nrow(combined) == 0) {
|
| 127 | ! |
return(combined) |
| 128 |
} |
|
| 129 | 3x |
keep_idx <- rep(FALSE, nrow(combined)) |
| 130 | 3x |
last_name <- NULL |
| 131 | 3x |
aggregator <- NULL |
| 132 | 3x |
out <- list() |
| 133 | ||
| 134 | 3x |
flush_row <- function(agg) {
|
| 135 |
# Fill preferred/formal with transcript_name if still empty |
|
| 136 | 7x |
if (is.na(agg$preferred_name) || trimws(agg$preferred_name) == "") {
|
| 137 | 1x |
agg$preferred_name <- agg$transcript_name |
| 138 |
} |
|
| 139 | 7x |
if (is.na(agg$formal_name) || trimws(agg$formal_name) == "") {
|
| 140 | 1x |
agg$formal_name <- agg$transcript_name |
| 141 |
} |
|
| 142 |
# Ensure participant_type |
|
| 143 | 7x |
if (is.na(agg$participant_type) || trimws(agg$participant_type) == "") {
|
| 144 | ! |
agg$participant_type <- "unknown" |
| 145 |
} |
|
| 146 | 7x |
agg |
| 147 |
} |
|
| 148 | ||
| 149 | 3x |
for (i in seq_len(nrow(combined))) {
|
| 150 | 8x |
row <- combined[i, , drop = FALSE] |
| 151 | 8x |
name <- ifelse(is.na(row$transcript_name), "", row$transcript_name) |
| 152 | 8x |
if (is.null(last_name) || !identical(tolower(last_name), tolower(name))) {
|
| 153 | 4x |
if (!is.null(aggregator)) out[[length(out) + 1]] <- flush_row(aggregator) |
| 154 | 7x |
aggregator <- row |
| 155 | 7x |
last_name <- name |
| 156 |
} else {
|
|
| 157 |
# Fill only missing fields from new row |
|
| 158 | 1x |
fields <- names(row) |
| 159 | 1x |
for (col in fields) {
|
| 160 | 6x |
if (is.na(aggregator[[col]]) || trimws(aggregator[[col]]) == "") {
|
| 161 | ! |
aggregator[[col]] <- row[[col]] |
| 162 |
} |
|
| 163 |
} |
|
| 164 |
} |
|
| 165 |
} |
|
| 166 | 3x |
if (!is.null(aggregator)) out[[length(out) + 1]] <- flush_row(aggregator) |
| 167 | ||
| 168 | 3x |
result <- do.call(rbind, out) |
| 169 | 3x |
.normalize_lookup_df(result) |
| 170 |
} |
|
| 171 | ||
| 172 |
#' Transactional Write with Backup |
|
| 173 |
#' |
|
| 174 |
#' Writes the lookup data frame to CSV with a timestamped backup of any |
|
| 175 |
#' existing file and an atomic replace (write to temp, then rename). |
|
| 176 |
#' |
|
| 177 |
#' @param df Data frame. The lookup to write (will be normalized). |
|
| 178 |
#' @param path Character. Target CSV path. |
|
| 179 |
#' |
|
| 180 |
#' @return Invisibly returns the path written. |
|
| 181 |
#' @export |
|
| 182 |
write_lookup_transactional <- function(df = NULL, path = NULL) {
|
|
| 183 |
# DEPRECATED: This function will be removed in the next version |
|
| 184 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 185 | 4x |
warning("Function 'write_lookup_transactional' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 186 | ||
| 187 | 4x |
if (!is.character(path) || length(path) != 1) {
|
| 188 | ! |
stop("path must be a single character string", call. = FALSE)
|
| 189 |
} |
|
| 190 | 4x |
df <- .normalize_lookup_df(df) |
| 191 | 4x |
dir.create(dirname(path), recursive = TRUE, showWarnings = FALSE) |
| 192 |
# Backup if exists |
|
| 193 | 4x |
if (file.exists(path)) {
|
| 194 | 1x |
backup <- paste0(path, ".backup.", format(Sys.time(), "%Y%m%d_%H%M%S")) |
| 195 |
# Try to create backup, but don't fail if backup directory doesn't exist |
|
| 196 | 1x |
ok <- tryCatch( |
| 197 |
{
|
|
| 198 | 1x |
file.copy(path, backup, overwrite = FALSE) |
| 199 |
}, |
|
| 200 | 1x |
error = function(e) {
|
| 201 |
# If backup fails, continue without backup (for vignette builds) |
|
| 202 | ! |
FALSE |
| 203 |
} |
|
| 204 |
) |
|
| 205 | 1x |
if (!isTRUE(ok)) {
|
| 206 |
# Don't fail the entire operation if backup fails |
|
| 207 | ! |
warning("Could not create backup for ", path, " - continuing without backup", call. = FALSE)
|
| 208 |
} |
|
| 209 |
} |
|
| 210 |
# Write to temp file first |
|
| 211 | 4x |
tmp <- paste0(path, ".tmp.", Sys.getpid()) |
| 212 | 4x |
utils::write.csv(df, tmp, row.names = FALSE, fileEncoding = "UTF-8") |
| 213 |
# Atomic-ish replace |
|
| 214 | 4x |
ok <- file.rename(tmp, path) |
| 215 | 4x |
if (!isTRUE(ok)) {
|
| 216 |
# Attempt fallback: remove and rename |
|
| 217 | ! |
if (file.exists(path)) unlink(path) |
| 218 | ! |
ok2 <- file.rename(tmp, path) |
| 219 | ! |
if (!isTRUE(ok2)) {
|
| 220 | ! |
unlink(tmp) |
| 221 | ! |
stop("Failed to write lookup file atomically: ", path, call. = FALSE)
|
| 222 |
} |
|
| 223 |
} |
|
| 224 | 4x |
invisible(path) |
| 225 |
} |
|
| 226 | ||
| 227 |
#' Conditionally Write Lookup (Read-Only Gate) |
|
| 228 |
#' |
|
| 229 |
#' Helper that writes the lookup only when `allow_write` is TRUE. Useful for |
|
| 230 |
#' scripts and Rmds to prevent accidental overwrites. When `allow_write` is |
|
| 231 |
#' FALSE, performs no side effects and returns `FALSE`. |
|
| 232 |
#' |
|
| 233 |
#' @param df Data frame. Lookup to write (normalized internally). |
|
| 234 |
#' @param path Character. Target CSV path. |
|
| 235 |
#' @param allow_write Logical. Default `FALSE` (read-only). |
|
| 236 |
#' |
|
| 237 |
#' @return Logical indicating whether a write occurred. |
|
| 238 |
#' @export |
|
| 239 |
conditionally_write_lookup <- function(df = NULL, path = NULL, allow_write = FALSE) {
|
|
| 240 |
# DEPRECATED: This function will be removed in the next version |
|
| 241 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 242 | 4x |
warning("Function 'conditionally_write_lookup' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 243 | ||
| 244 | 4x |
if (!isTRUE(allow_write)) {
|
| 245 | 3x |
return(FALSE) |
| 246 |
} |
|
| 247 | 1x |
write_lookup_transactional(df, path) |
| 248 | 1x |
TRUE |
| 249 |
} |
|
| 250 | ||
| 251 |
#' Ensure Instructor Rows (Pure Merge) |
|
| 252 |
#' |
|
| 253 |
#' Returns a merged lookup that includes instructor rows for a given |
|
| 254 |
#' instructor name, preserving all existing rows. No side effects. |
|
| 255 |
#' |
|
| 256 |
#' @param existing_df Data frame. Existing lookup. |
|
| 257 |
#' @param instructor_name Character. Instructor display name. |
|
| 258 |
#' |
|
| 259 |
#' @return A merged lookup including instructor rows. |
|
| 260 |
#' @export |
|
| 261 |
ensure_instructor_rows <- function(existing_df = NULL, instructor_name = NULL) {
|
|
| 262 |
# DEPRECATED: This function will be removed in the next version |
|
| 263 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 264 | 1x |
warning("Function 'ensure_instructor_rows' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 265 | ||
| 266 | 1x |
if (!is.character(instructor_name) || length(instructor_name) != 1) {
|
| 267 | ! |
stop("instructor_name must be a single character string", call. = FALSE)
|
| 268 |
} |
|
| 269 | 1x |
base <- .normalize_lookup_df(existing_df) |
| 270 | 1x |
instructor_df <- data.frame( |
| 271 | 1x |
transcript_name = instructor_name, |
| 272 | 1x |
preferred_name = instructor_name, |
| 273 | 1x |
formal_name = instructor_name, |
| 274 | 1x |
participant_type = "instructor", |
| 275 | 1x |
student_id = "INSTRUCTOR", |
| 276 | 1x |
notes = "Primary instructor name - add variations manually if needed", |
| 277 | 1x |
stringsAsFactors = FALSE |
| 278 |
) |
|
| 279 | 1x |
merge_lookup_preserve(base, instructor_df) |
| 280 |
} |
| 1 |
#' Function Audit System for Issue #393 Phase 3 |
|
| 2 |
#' |
|
| 3 |
#' This module provides comprehensive function auditing, categorization, and |
|
| 4 |
#' dependency mapping for the scope reduction implementation. |
|
| 5 |
#' |
|
| 6 |
#' @keywords internal |
|
| 7 |
#' @noRd |
|
| 8 |
#' |
|
| 9 |
#' Create Comprehensive Function Inventory |
|
| 10 |
#' |
|
| 11 |
#' @return List containing function inventory and metadata |
|
| 12 |
#' @keywords internal |
|
| 13 |
create_function_inventory <- function() {
|
|
| 14 |
# DEPRECATED: This function will be removed in the next version |
|
| 15 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 16 | ! |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 17 | ! |
warning("Function 'create_function_inventory' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 18 |
} |
|
| 19 | ||
| 20 | ! |
tryCatch( |
| 21 |
{
|
|
| 22 |
# Get all R files |
|
| 23 | ! |
r_files <- list.files("R/", pattern = "\\.R$", full.names = FALSE)
|
| 24 | ||
| 25 |
# Get exported functions from NAMESPACE |
|
| 26 | ! |
namespace_exports <- character(0) |
| 27 | ! |
if (file.exists("NAMESPACE")) {
|
| 28 | ! |
namespace_content <- readLines("NAMESPACE")
|
| 29 | ! |
export_lines <- grep("^export\\(", namespace_content, value = TRUE)
|
| 30 | ! |
namespace_exports <- gsub("^export\\(([^)]+)\\)", "\\1", export_lines)
|
| 31 |
# Remove quotes and clean up |
|
| 32 | ! |
namespace_exports <- gsub('"', "", namespace_exports)
|
| 33 | ! |
namespace_exports <- gsub("'", "", namespace_exports)
|
| 34 |
} |
|
| 35 | ||
| 36 |
# Get function documentation files |
|
| 37 | ! |
man_files <- list.files("man/", pattern = "\\.Rd$", full.names = FALSE)
|
| 38 | ! |
documented_functions <- gsub("\\.Rd$", "", man_files)
|
| 39 | ||
| 40 |
# Create inventory |
|
| 41 | ! |
inventory <- list( |
| 42 | ! |
metadata = list( |
| 43 | ! |
audit_date = Sys.Date(), |
| 44 | ! |
audit_time = Sys.time(), |
| 45 | ! |
total_r_files = length(r_files), |
| 46 | ! |
total_man_files = length(man_files), |
| 47 | ! |
total_exports = length(namespace_exports) |
| 48 |
), |
|
| 49 | ! |
r_files = r_files, |
| 50 | ! |
namespace_exports = namespace_exports, |
| 51 | ! |
documented_functions = documented_functions, |
| 52 | ! |
export_status = list( |
| 53 | ! |
exported_and_documented = intersect(namespace_exports, documented_functions), |
| 54 | ! |
exported_not_documented = setdiff(namespace_exports, documented_functions), |
| 55 | ! |
documented_not_exported = setdiff(documented_functions, namespace_exports) |
| 56 |
) |
|
| 57 |
) |
|
| 58 | ||
| 59 | ! |
return(inventory) |
| 60 |
}, |
|
| 61 | ! |
error = function(e) {
|
| 62 | ! |
warning("Failed to create function inventory: ", e$message)
|
| 63 | ! |
return(list(error = e$message)) |
| 64 |
} |
|
| 65 |
) |
|
| 66 |
} |
|
| 67 | ||
| 68 |
#' Categorize Functions by Type and Purpose |
|
| 69 |
#' |
|
| 70 |
#' @param inventory Function inventory from create_function_inventory() |
|
| 71 |
#' @return Categorized function list |
|
| 72 |
#' @keywords internal |
|
| 73 |
categorize_functions <- function(inventory) {
|
|
| 74 |
# DEPRECATED: This function will be removed in the next version |
|
| 75 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 76 |
if (Sys.getenv("TESTTHAT") != "true") {
|
|
| 77 |
warning("Function 'categorize_functions' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
|
| 78 |
} |
|
| 79 | ||
| 80 |
if (is.null(inventory) || "error" %in% names(inventory)) {
|
|
| 81 |
return(list(error = "Invalid inventory provided")) |
|
| 82 |
} |
|
| 83 | ||
| 84 |
# Define function categories based on naming patterns and purpose |
|
| 85 |
categories <- list( |
|
| 86 |
essential = c( |
|
| 87 |
"analyze_transcripts", "process_zoom_transcript", "load_zoom_transcript", |
|
| 88 |
"consolidate_transcript", "summarize_transcript_metrics", "plot_users", |
|
| 89 |
"write_metrics", "ensure_privacy", "set_privacy_defaults", "privacy_audit", |
|
| 90 |
"load_roster", "load_session_mapping", "safe_name_matching_workflow", |
|
| 91 |
"validate_schema", "validate_privacy_compliance" |
|
| 92 |
), |
|
| 93 |
deprecated = c( |
|
| 94 |
"add_dead_air_rows", "make_transcripts_session_summary_df", "make_sections_df", |
|
| 95 |
"join_transcripts_list", "validate_ethical_use", "create_ethical_use_report", |
|
| 96 |
"audit_ethical_usage", "make_blank_cancelled_classes_df", "load_section_names_lookup", |
|
| 97 |
"create_session_mapping", "is_verbose", "diag_message", "make_blank_section_names_lookup_csv", |
|
| 98 |
"process_ideal_course_batch", "compare_ideal_sessions", "validate_ideal_scenarios", |
|
| 99 |
"extract_character_values", "detect_privacy_violations", "normalize_name_for_matching", |
|
| 100 |
"write_engagement_metrics", "log_privacy_operation", "make_names_to_clean_df", |
|
| 101 |
"export_ideal_transcripts_csv", "export_ideal_transcripts_json", "export_ideal_transcripts_excel", |
|
| 102 |
"export_ideal_transcripts_summary", "generate_ferpa_report", "check_data_retention_policy", |
|
| 103 |
"make_roster_small", "classify_participants" |
|
| 104 |
), |
|
| 105 |
advanced = c( |
|
| 106 |
"analyze_multi_session_attendance", "anonymize_educational_data", |
|
| 107 |
"calculate_content_similarity", "detect_duplicate_transcripts", |
|
| 108 |
"ensure_instructor_rows", "create_analysis_config", "create_course_info", |
|
| 109 |
"detect_unmatched_names", "prompt_name_matching", "run_student_reports" |
|
| 110 |
), |
|
| 111 |
utility = c( |
|
| 112 |
"conditionally_write_lookup", "get_essential_functions", "get_deprecated_functions" |
|
| 113 |
) |
|
| 114 |
) |
|
| 115 | ||
| 116 |
# Categorize each exported function |
|
| 117 |
exported_functions <- inventory$namespace_exports |
|
| 118 |
categorization <- list() |
|
| 119 | ||
| 120 |
for (func in exported_functions) {
|
|
| 121 |
category <- "uncategorized" |
|
| 122 |
for (cat_name in names(categories)) {
|
|
| 123 |
if (func %in% categories[[cat_name]]) {
|
|
| 124 |
category <- cat_name |
|
| 125 |
break |
|
| 126 |
} |
|
| 127 |
} |
|
| 128 |
categorization[[func]] <- category |
|
| 129 |
} |
|
| 130 | ||
| 131 |
# Create summary |
|
| 132 |
summary <- list( |
|
| 133 |
total_exported = length(exported_functions), |
|
| 134 |
essential_count = sum(categorization == "essential"), |
|
| 135 |
deprecated_count = sum(categorization == "deprecated"), |
|
| 136 |
advanced_count = sum(categorization == "advanced"), |
|
| 137 |
utility_count = sum(categorization == "utility"), |
|
| 138 |
uncategorized_count = sum(categorization == "uncategorized"), |
|
| 139 |
categorization = categorization |
|
| 140 |
) |
|
| 141 | ||
| 142 |
return(summary) |
|
| 143 |
} |
|
| 144 | ||
| 145 |
#' Map Function Dependencies |
|
| 146 |
#' |
|
| 147 |
#' @param inventory Function inventory from create_function_inventory() |
|
| 148 |
#' @return Dependency map |
|
| 149 |
#' @keywords internal |
|
| 150 |
map_function_dependencies <- function(inventory) {
|
|
| 151 |
# DEPRECATED: This function will be removed in the next version |
|
| 152 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 153 | ! |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 154 | ! |
warning("Function 'map_function_dependencies' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 155 |
} |
|
| 156 | ||
| 157 | ! |
if (is.null(inventory) || "error" %in% names(inventory)) {
|
| 158 | ! |
return(list(error = "Invalid inventory provided")) |
| 159 |
} |
|
| 160 | ||
| 161 |
# Simple dependency mapping based on function calls |
|
| 162 |
# This is a simplified approach - in practice, you might want more sophisticated parsing |
|
| 163 | ||
| 164 | ! |
dependencies <- list( |
| 165 | ! |
essential_functions = list( |
| 166 | ! |
analyze_transcripts = c("process_zoom_transcript", "consolidate_transcript", "summarize_transcript_metrics"),
|
| 167 | ! |
process_zoom_transcript = c("load_zoom_transcript", "ensure_privacy"),
|
| 168 | ! |
consolidate_transcript = c("load_zoom_transcript", "ensure_privacy"),
|
| 169 | ! |
summarize_transcript_metrics = c("consolidate_transcript", "plot_users"),
|
| 170 | ! |
plot_users = c("ensure_privacy", "write_metrics")
|
| 171 |
), |
|
| 172 | ! |
deprecated_functions = list( |
| 173 | ! |
add_dead_air_rows = c("process_zoom_transcript"),
|
| 174 | ! |
make_transcripts_session_summary_df = c("analyze_transcripts"),
|
| 175 | ! |
make_sections_df = c("load_roster"),
|
| 176 | ! |
join_transcripts_list = c("load_zoom_transcript")
|
| 177 |
) |
|
| 178 |
) |
|
| 179 | ||
| 180 | ! |
return(dependencies) |
| 181 |
} |
|
| 182 | ||
| 183 |
#' Generate Function Audit Report |
|
| 184 |
#' |
|
| 185 |
#' @param inventory Function inventory |
|
| 186 |
#' @param categorization Function categorization |
|
| 187 |
#' @param dependencies Function dependencies |
|
| 188 |
#' @return Formatted audit report |
|
| 189 |
#' @keywords internal |
|
| 190 |
generate_function_audit_report <- function(inventory, categorization, dependencies) {
|
|
| 191 |
# DEPRECATED: This function will be removed in the next version |
|
| 192 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 193 | ! |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 194 | ! |
warning("Function 'generate_function_audit_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 195 |
} |
|
| 196 | ||
| 197 | ! |
if (is.null(inventory) || "error" %in% names(inventory)) {
|
| 198 | ! |
return("ERROR: Invalid inventory provided")
|
| 199 |
} |
|
| 200 | ||
| 201 | ! |
report <- paste0( |
| 202 | ! |
"=== FUNCTION AUDIT REPORT ===\n", |
| 203 | ! |
"Audit Date: ", format(Sys.Date(), "%Y-%m-%d"), "\n", |
| 204 | ! |
"Audit Time: ", format(Sys.time(), "%Y-%m-%d %H:%M:%S"), "\n\n", |
| 205 | ! |
"INVENTORY SUMMARY:\n", |
| 206 | ! |
" Total R Files: ", inventory$metadata$total_r_files, "\n", |
| 207 | ! |
" Total Documentation Files: ", inventory$metadata$total_man_files, "\n", |
| 208 | ! |
" Total Exported Functions: ", inventory$metadata$total_exports, "\n\n", |
| 209 | ! |
"EXPORT STATUS:\n", |
| 210 | ! |
" Exported and Documented: ", length(inventory$export_status$exported_and_documented), "\n", |
| 211 | ! |
" Exported but Not Documented: ", length(inventory$export_status$exported_not_documented), "\n", |
| 212 | ! |
" Documented but Not Exported: ", length(inventory$export_status$documented_not_exported), "\n\n" |
| 213 |
) |
|
| 214 | ||
| 215 | ! |
if (!is.null(categorization) && !("error" %in% names(categorization))) {
|
| 216 | ! |
report <- paste0( |
| 217 | ! |
report, |
| 218 | ! |
"FUNCTION CATEGORIZATION:\n", |
| 219 | ! |
" Essential Functions: ", categorization$essential_count, "\n", |
| 220 | ! |
" Deprecated Functions: ", categorization$deprecated_count, "\n", |
| 221 | ! |
" Advanced Functions: ", categorization$advanced_count, "\n", |
| 222 | ! |
" Utility Functions: ", categorization$utility_count, "\n", |
| 223 | ! |
" Uncategorized Functions: ", categorization$uncategorized_count, "\n\n" |
| 224 |
) |
|
| 225 |
} |
|
| 226 | ||
| 227 | ! |
return(report) |
| 228 |
} |
|
| 229 | ||
| 230 |
#' Save Function Audit Report |
|
| 231 |
#' |
|
| 232 |
#' @param inventory Function inventory |
|
| 233 |
#' @param categorization Function categorization |
|
| 234 |
#' @param dependencies Function dependencies |
|
| 235 |
#' @param output_file Output file path |
|
| 236 |
#' @return TRUE if successful |
|
| 237 |
#' @keywords internal |
|
| 238 |
save_function_audit_report <- function(inventory, categorization, dependencies, |
|
| 239 |
output_file = "function_audit_report.txt") {
|
|
| 240 |
# DEPRECATED: This function will be removed in the next version |
|
| 241 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 242 | ! |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 243 | ! |
warning("Function 'save_function_audit_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 244 |
} |
|
| 245 | ||
| 246 | ! |
tryCatch( |
| 247 |
{
|
|
| 248 | ! |
report <- generate_function_audit_report(inventory, categorization, dependencies) |
| 249 | ! |
writeLines(report, output_file) |
| 250 | ! |
message("Function audit report saved to: ", output_file)
|
| 251 | ! |
TRUE |
| 252 |
}, |
|
| 253 | ! |
error = function(e) {
|
| 254 | ! |
warning("Failed to save function audit report: ", e$message)
|
| 255 | ! |
FALSE |
| 256 |
} |
|
| 257 |
) |
|
| 258 |
} |
| 1 |
#' Prompt User for Name Matching |
|
| 2 |
#' |
|
| 3 |
#' Provides safe user guidance for name matching when unmatched names are detected. |
|
| 4 |
#' This function maintains privacy by using the existing `make_blank_section_names_lookup_csv()` |
|
| 5 |
#' function and provides clear instructions for manual name mapping. |
|
| 6 |
#' |
|
| 7 |
#' @param unmatched_names Character vector of unmatched names that need mapping |
|
| 8 |
#' @param privacy_level Privacy level for the session. One of |
|
| 9 |
#' `c("ferpa_strict", "ferpa_standard", "mask", "none")`.
|
|
| 10 |
#' Defaults to `getOption("zoomstudentengagement.privacy_level", "mask")`.
|
|
| 11 |
#' @param data_folder Data folder path for saving the lookup file |
|
| 12 |
#' @param section_names_lookup_file Name of the lookup file to create |
|
| 13 |
#' @param include_instructions Logical, whether to include detailed instructions |
|
| 14 |
#' in the output. Defaults to TRUE. |
|
| 15 |
#' |
|
| 16 |
#' @return Invisibly returns the path to the created lookup file |
|
| 17 |
#' @export |
|
| 18 |
#' |
|
| 19 |
#' @examples |
|
| 20 |
#' # Prompt for name matching (privacy-safe). Write artifacts to tempdir(). |
|
| 21 |
#' unmatched <- c("Dr. Smith", "Tom", "Guest1")
|
|
| 22 |
#' prompt_name_matching(unmatched, data_folder = tempdir()) |
|
| 23 |
#' |
|
| 24 |
#' # Create lookup file with custom settings |
|
| 25 |
#' prompt_name_matching( |
|
| 26 |
#' unmatched_names = c("John Doe", "Jane Smith"),
|
|
| 27 |
#' data_folder = tempdir(), |
|
| 28 |
#' section_names_lookup_file = "section_names_lookup.csv" |
|
| 29 |
#' ) |
|
| 30 |
prompt_name_matching <- function(unmatched_names = NULL, |
|
| 31 |
privacy_level = getOption( |
|
| 32 |
"zoomstudentengagement.privacy_level", |
|
| 33 |
"mask" |
|
| 34 |
), |
|
| 35 |
data_folder = ".", |
|
| 36 |
section_names_lookup_file = "section_names_lookup.csv", |
|
| 37 |
include_instructions = TRUE) {
|
|
| 38 |
# DEPRECATED: This function will be removed in the next version |
|
| 39 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 40 | 9x |
warning("Function 'prompt_name_matching' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 41 | ||
| 42 |
# Validate inputs |
|
| 43 | 9x |
if (!is.character(unmatched_names)) {
|
| 44 | 1x |
stop("unmatched_names must be a character vector", call. = FALSE)
|
| 45 |
} |
|
| 46 | ||
| 47 | 8x |
valid_levels <- c("ferpa_strict", "ferpa_standard", "mask", "none")
|
| 48 | 8x |
if (!privacy_level %in% valid_levels) {
|
| 49 | 1x |
stop("Invalid privacy_level. Must be one of: ",
|
| 50 | 1x |
paste(valid_levels, collapse = ", "), |
| 51 | 1x |
call. = FALSE |
| 52 |
) |
|
| 53 |
} |
|
| 54 | ||
| 55 | 7x |
if (!is.character(data_folder) || length(data_folder) != 1) {
|
| 56 | 1x |
stop("data_folder must be a single character string", call. = FALSE)
|
| 57 |
} |
|
| 58 | ||
| 59 | 6x |
if (!is.character(section_names_lookup_file) || length(section_names_lookup_file) != 1) {
|
| 60 | 1x |
stop("section_names_lookup_file must be a single character string", call. = FALSE)
|
| 61 |
} |
|
| 62 | ||
| 63 | 5x |
if (!is.logical(include_instructions) || length(include_instructions) != 1) {
|
| 64 | 1x |
stop("include_instructions must be a single logical value", call. = FALSE)
|
| 65 |
} |
|
| 66 | ||
| 67 |
# If no unmatched names, return early |
|
| 68 | 4x |
if (length(unmatched_names) == 0) {
|
| 69 | 1x |
diag_message("No unmatched names found. Name matching is complete.")
|
| 70 | 1x |
return(invisible(NULL)) |
| 71 |
} |
|
| 72 | ||
| 73 |
# Create data folder if it doesn't exist |
|
| 74 | 3x |
if (!dir.exists(data_folder)) {
|
| 75 | ! |
dir.create(data_folder, recursive = TRUE) |
| 76 | ! |
diag_message("Created data folder: ", data_folder)
|
| 77 |
} |
|
| 78 | ||
| 79 |
# Generate privacy-safe guidance |
|
| 80 | 3x |
guidance <- generate_name_matching_guidance( |
| 81 | 3x |
unmatched_names, |
| 82 | 3x |
privacy_level, |
| 83 | 3x |
include_instructions |
| 84 |
) |
|
| 85 | ||
| 86 |
# Display guidance to user (quiet by default) |
|
| 87 | 3x |
diag_cat("\n", guidance, "\n", sep = "")
|
| 88 | ||
| 89 |
# Create the lookup file using existing function |
|
| 90 | 3x |
lookup_file_path <- file.path(data_folder, section_names_lookup_file) |
| 91 | ||
| 92 |
# Use existing function to create blank template |
|
| 93 | 3x |
lookup_template <- make_blank_section_names_lookup_csv() |
| 94 | ||
| 95 |
# Save the template to the specified file |
|
| 96 | 3x |
readr::write_csv(lookup_template, lookup_file_path) |
| 97 | ||
| 98 | 3x |
diag_message("\nCreated lookup file: ", lookup_file_path)
|
| 99 | 3x |
diag_message("Please edit this file to map the unmatched names, then re-run your analysis.")
|
| 100 | ||
| 101 |
# Return the file path invisibly |
|
| 102 | 3x |
invisible(lookup_file_path) |
| 103 |
} |
|
| 104 | ||
| 105 |
#' Generate Name Matching Guidance |
|
| 106 |
#' |
|
| 107 |
#' Internal function to generate privacy-safe guidance for name matching. |
|
| 108 |
#' |
|
| 109 |
#' @param unmatched_names Character vector of unmatched names |
|
| 110 |
#' @param privacy_level Privacy level for the session |
|
| 111 |
#' @param include_instructions Whether to include detailed instructions |
|
| 112 |
#' |
|
| 113 |
#' @return Character string with guidance |
|
| 114 |
#' @keywords internal |
|
| 115 |
generate_name_matching_guidance <- function(unmatched_names, privacy_level, include_instructions) {
|
|
| 116 |
# DEPRECATED: This function will be removed in the next version |
|
| 117 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 118 | 7x |
warning("Function 'generate_name_matching_guidance' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 119 | ||
| 120 |
# Count unmatched names |
|
| 121 | 7x |
n_unmatched <- length(unmatched_names) |
| 122 | ||
| 123 |
# Base message |
|
| 124 | 7x |
if (n_unmatched == 1) {
|
| 125 | 5x |
base_msg <- "Found 1 unmatched name that needs manual mapping." |
| 126 |
} else {
|
|
| 127 | 2x |
base_msg <- paste("Found", n_unmatched, "unmatched names that need manual mapping.")
|
| 128 |
} |
|
| 129 | ||
| 130 |
# Privacy warning if needed |
|
| 131 | 7x |
privacy_msg <- "" |
| 132 | 7x |
if (!identical(privacy_level, "none")) {
|
| 133 | 6x |
privacy_msg <- paste( |
| 134 | 6x |
"\n*** PRIVACY WARNING:", |
| 135 | 6x |
"Real names will be shown below for matching purposes only.", |
| 136 | 6x |
"These names will be masked in all final outputs." |
| 137 |
) |
|
| 138 |
} |
|
| 139 | ||
| 140 |
# Show unmatched names (this is the only place real names should appear) |
|
| 141 | 7x |
names_msg <- paste( |
| 142 | 7x |
"\nUnmatched names:", |
| 143 | 7x |
paste(unmatched_names, collapse = ", ") |
| 144 |
) |
|
| 145 | ||
| 146 |
# Instructions |
|
| 147 | 7x |
instructions_msg <- "" |
| 148 | 7x |
if (include_instructions) {
|
| 149 | 6x |
instructions_msg <- paste( |
| 150 | 6x |
"\n\n*** INSTRUCTIONS:", |
| 151 | 6x |
"1. Open the created 'section_names_lookup.csv' file", |
| 152 | 6x |
"2. Add rows for each unmatched name above", |
| 153 | 6x |
"3. Map each transcript name to the correct roster name", |
| 154 | 6x |
"4. Set 'participant_type' to one of:", |
| 155 | 6x |
" - 'instructor' for faculty/staff", |
| 156 | 6x |
" - 'enrolled_student' for students on roster", |
| 157 | 6x |
" - 'guest' for non-enrolled participants", |
| 158 | 6x |
"5. Save the file and re-run your analysis", |
| 159 |
"", |
|
| 160 | 6x |
"*** TIP: Use consistent naming across sessions for better matching", |
| 161 | 6x |
sep = "\n" |
| 162 |
) |
|
| 163 |
} |
|
| 164 | ||
| 165 |
# Combine all messages |
|
| 166 | 7x |
paste(base_msg, privacy_msg, names_msg, instructions_msg, sep = "") |
| 167 |
} |
|
| 168 | ||
| 169 |
#' Detect Unmatched Names |
|
| 170 |
#' |
|
| 171 |
#' Identifies names in transcript data that are not matched against roster data |
|
| 172 |
#' or existing name mappings. This function works with real names in memory only |
|
| 173 |
#' and returns privacy-safe results. |
|
| 174 |
#' |
|
| 175 |
#' @param transcript_data Data frame containing transcript data with name columns |
|
| 176 |
#' @param roster_data Data frame containing roster data with name columns |
|
| 177 |
#' @param name_mappings Data frame containing existing name mappings |
|
| 178 |
#' @param privacy_level Privacy level for the session. One of |
|
| 179 |
#' `c("ferpa_strict", "ferpa_standard", "mask", "none")`.
|
|
| 180 |
#' Defaults to `getOption("zoomstudentengagement.privacy_level", "mask")`.
|
|
| 181 |
#' |
|
| 182 |
#' @return Character vector of unmatched names (real names only if privacy = "none") |
|
| 183 |
#' @export |
|
| 184 |
#' |
|
| 185 |
#' @examples |
|
| 186 |
#' # Detect unmatched names |
|
| 187 |
#' transcript_df <- tibble::tibble( |
|
| 188 |
#' transcript_name = c("Dr. Smith", "John Doe", "Guest1"),
|
|
| 189 |
#' course_section = c("101.A", "101.A", "101.A")
|
|
| 190 |
#' ) |
|
| 191 |
#' roster_df <- tibble::tibble( |
|
| 192 |
#' first_last = c("John Doe", "Jane Smith"),
|
|
| 193 |
#' course_section = c("101.A", "101.A")
|
|
| 194 |
#' ) |
|
| 195 |
#' unmatched <- detect_unmatched_names(transcript_df, roster_df) |
|
| 196 |
detect_unmatched_names <- function(transcript_data = NULL, |
|
| 197 |
roster_data = NULL, |
|
| 198 |
name_mappings = NULL, |
|
| 199 |
privacy_level = getOption( |
|
| 200 |
"zoomstudentengagement.privacy_level", |
|
| 201 |
"mask" |
|
| 202 |
)) {
|
|
| 203 |
# Validate inputs |
|
| 204 | 20x |
if (!is.data.frame(transcript_data)) {
|
| 205 | 1x |
stop("transcript_data must be a data frame", call. = FALSE)
|
| 206 |
} |
|
| 207 | 19x |
if (!is.data.frame(roster_data)) {
|
| 208 | 1x |
stop("roster_data must be a data frame", call. = FALSE)
|
| 209 |
} |
|
| 210 | ||
| 211 | 18x |
valid_levels <- c("ferpa_strict", "ferpa_standard", "mask", "none")
|
| 212 | 18x |
if (!privacy_level %in% valid_levels) {
|
| 213 | 1x |
stop("Invalid privacy_level. Must be one of: ",
|
| 214 | 1x |
paste(valid_levels, collapse = ", "), |
| 215 | 1x |
call. = FALSE |
| 216 |
) |
|
| 217 |
} |
|
| 218 | ||
| 219 |
# Extract transcript names |
|
| 220 | 17x |
transcript_names <- extract_transcript_names(transcript_data) |
| 221 | ||
| 222 |
# Extract roster names |
|
| 223 | 17x |
roster_names <- extract_roster_names(roster_data) |
| 224 | ||
| 225 |
# Extract mapped names (if provided) |
|
| 226 | 17x |
mapped_names <- character(0) |
| 227 | 17x |
if (!is.null(name_mappings) && is.data.frame(name_mappings)) {
|
| 228 | 15x |
mapped_names <- extract_mapped_names(name_mappings) |
| 229 |
} |
|
| 230 | ||
| 231 |
# Combine all known names |
|
| 232 | 17x |
known_names <- unique(c(roster_names, mapped_names)) |
| 233 | ||
| 234 |
# Find unmatched names |
|
| 235 | 17x |
unmatched_names <- setdiff(transcript_names, known_names) |
| 236 | ||
| 237 |
# Remove empty or NA names |
|
| 238 | 17x |
unmatched_names <- unmatched_names[!is.na(unmatched_names) & nchar(trimws(unmatched_names)) > 0] |
| 239 | ||
| 240 |
# If privacy is enabled, return hashed versions |
|
| 241 | 17x |
if (!identical(privacy_level, "none")) {
|
| 242 | 2x |
unmatched_names <- hash_name_consistently(unmatched_names) |
| 243 |
} |
|
| 244 | ||
| 245 |
# Return unique unmatched names |
|
| 246 | 17x |
unique(unmatched_names) |
| 247 |
} |
|
| 248 | ||
| 249 |
#' Extract Transcript Names |
|
| 250 |
#' |
|
| 251 |
#' Internal function to extract names from transcript data. |
|
| 252 |
#' |
|
| 253 |
#' @param transcript_data Data frame containing transcript data |
|
| 254 |
#' |
|
| 255 |
#' @return Character vector of transcript names |
|
| 256 |
#' @keywords internal |
|
| 257 |
extract_transcript_names <- function(transcript_data) {
|
|
| 258 |
# DEPRECATED: This function will be removed in the next version |
|
| 259 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 260 | 44x |
warning("Function 'extract_transcript_names' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 261 | ||
| 262 |
# Look for common name columns in transcript data |
|
| 263 | 44x |
name_columns <- c("transcript_name", "name", "speaker_name", "participant_name")
|
| 264 | 44x |
found_columns <- intersect(name_columns, names(transcript_data)) |
| 265 | ||
| 266 | 44x |
if (length(found_columns) == 0) {
|
| 267 | 5x |
return(character(0)) |
| 268 |
} |
|
| 269 | ||
| 270 |
# Use the first found column |
|
| 271 | 39x |
names <- transcript_data[[found_columns[1]]] |
| 272 | ||
| 273 |
# Convert to character and clean |
|
| 274 | 39x |
names <- as.character(names) |
| 275 | 39x |
names <- names[!is.na(names) & nchar(trimws(names)) > 0] |
| 276 | ||
| 277 | 39x |
names |
| 278 |
} |
|
| 279 | ||
| 280 |
#' Extract Roster Names |
|
| 281 |
#' |
|
| 282 |
#' Internal function to extract names from roster data. |
|
| 283 |
#' |
|
| 284 |
#' @param roster_data Data frame containing roster data |
|
| 285 |
#' |
|
| 286 |
#' @return Character vector of roster names |
|
| 287 |
#' @keywords internal |
|
| 288 |
extract_roster_names <- function(roster_data) {
|
|
| 289 |
# DEPRECATED: This function will be removed in the next version |
|
| 290 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 291 | 42x |
warning("Function 'extract_roster_names' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 292 | ||
| 293 |
# Look for common name columns in roster data |
|
| 294 | 42x |
name_columns <- c("first_last", "preferred_name", "formal_name", "name", "student_name")
|
| 295 | 42x |
found_columns <- intersect(name_columns, names(roster_data)) |
| 296 | ||
| 297 | 42x |
if (length(found_columns) == 0) {
|
| 298 | 3x |
return(character(0)) |
| 299 |
} |
|
| 300 | ||
| 301 |
# Use the first found column |
|
| 302 | 39x |
names <- roster_data[[found_columns[1]]] |
| 303 | ||
| 304 |
# Convert to character and clean |
|
| 305 | 39x |
names <- as.character(names) |
| 306 | 39x |
names <- names[!is.na(names) & nchar(trimws(names)) > 0] |
| 307 | ||
| 308 | 39x |
names |
| 309 |
} |
|
| 310 | ||
| 311 |
#' Extract Mapped Names |
|
| 312 |
#' |
|
| 313 |
#' Internal function to extract names from name mappings. |
|
| 314 |
#' |
|
| 315 |
#' @param name_mappings Data frame containing name mappings |
|
| 316 |
#' |
|
| 317 |
#' @return Character vector of mapped names |
|
| 318 |
#' @keywords internal |
|
| 319 |
extract_mapped_names <- function(name_mappings) {
|
|
| 320 |
# DEPRECATED: This function will be removed in the next version |
|
| 321 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 322 | 20x |
warning("Function 'extract_mapped_names' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 323 | ||
| 324 |
# Look for common name columns in mappings |
|
| 325 | 20x |
name_columns <- c("preferred_name", "formal_name", "transcript_name", "name")
|
| 326 | 20x |
found_columns <- intersect(name_columns, names(name_mappings)) |
| 327 | ||
| 328 | 20x |
if (length(found_columns) == 0) {
|
| 329 | 4x |
return(character(0)) |
| 330 |
} |
|
| 331 | ||
| 332 |
# Use the first found column |
|
| 333 | 16x |
names <- name_mappings[[found_columns[1]]] |
| 334 | ||
| 335 |
# Convert to character and clean |
|
| 336 | 16x |
names <- as.character(names) |
| 337 | 16x |
names <- names[!is.na(names) & nchar(trimws(names)) > 0] |
| 338 | ||
| 339 | 16x |
names |
| 340 |
} |
| 1 |
#' Plot Users |
|
| 2 |
#' |
|
| 3 |
#' Unified plotting function for engagement metrics with privacy-aware options. |
|
| 4 |
#' |
|
| 5 |
#' @param data A tibble with engagement metrics. Must contain the selected metric |
|
| 6 |
#' and a column representing student identity (default `preferred_name`). |
|
| 7 |
#' @param metric Column name of the metric to plot. Default: "session_ct". |
|
| 8 |
#' @param student_col Column name to use for student labels. Default: "preferred_name". |
|
| 9 |
#' @param facet_by One of c("section", "transcript_file", "none"). If the chosen
|
|
| 10 |
#' column is not present or set to "none", no faceting is applied. Default: "section". |
|
| 11 |
#' @param mask_by One of c("name", "rank"). If "rank", names are replaced with
|
|
| 12 |
#' per-section rank labels using `mask_user_names_by_metric()`. Default: "name". |
|
| 13 |
#' @param privacy_level Privacy level passed to `ensure_privacy()` when `mask_by = "name"`. |
|
| 14 |
#' Defaults to `getOption("zoomstudentengagement.privacy_level", "mask")`.
|
|
| 15 |
#' @param metrics_lookup_df Optional tibble with `metric` and `description` columns |
|
| 16 |
#' to annotate plots. Defaults to `make_metrics_lookup_df()` if available. |
|
| 17 |
#' |
|
| 18 |
#' @return A `ggplot` object. |
|
| 19 |
#' @export |
|
| 20 |
#' |
|
| 21 |
#' @examples |
|
| 22 |
#' # Minimal example |
|
| 23 |
#' # plot_users(df, metric = "session_ct") |
|
| 24 |
plot_users <- function( |
|
| 25 |
data = NULL, |
|
| 26 |
metric = "session_ct", |
|
| 27 |
student_col = "name", |
|
| 28 |
facet_by = c("section", "transcript_file", "none"),
|
|
| 29 |
mask_by = c("name", "rank"),
|
|
| 30 |
privacy_level = getOption("zoomstudentengagement.privacy_level", "mask"),
|
|
| 31 |
metrics_lookup_df = NULL) {
|
|
| 32 | 118x |
facet_by <- match.arg(facet_by) |
| 33 | 117x |
mask_by <- match.arg(mask_by) |
| 34 | ||
| 35 |
# Validate input |
|
| 36 | 116x |
if (!tibble::is_tibble(data)) {
|
| 37 | ! |
stop("`data` must be a tibble.")
|
| 38 |
} |
|
| 39 | 116x |
if (!metric %in% names(data)) {
|
| 40 |
# Support aliasing between old/new percentage names |
|
| 41 | 4x |
alias_map <- c( |
| 42 | 4x |
n_perc = "perc_n", |
| 43 | 4x |
duration_perc = "perc_duration", |
| 44 | 4x |
wordcount_perc = "perc_wordcount", |
| 45 | 4x |
perc_n = "n_perc", |
| 46 | 4x |
perc_duration = "duration_perc", |
| 47 | 4x |
perc_wordcount = "wordcount_perc" |
| 48 |
) |
|
| 49 | 4x |
if (metric %in% names(alias_map) && alias_map[[metric]] %in% names(data)) {
|
| 50 | ! |
metric <- alias_map[[metric]] |
| 51 |
} else {
|
|
| 52 | 4x |
stop(sprintf("Metric '%s' not found in data", metric))
|
| 53 |
} |
|
| 54 |
} |
|
| 55 | 111x |
if (!student_col %in% names(data)) {
|
| 56 |
# Fallback to common alternate |
|
| 57 | 99x |
if ("preferred_name" %in% names(data)) {
|
| 58 | 99x |
student_col <- "preferred_name" |
| 59 | ! |
} else if ("name" %in% names(data)) {
|
| 60 | ! |
student_col <- "name" |
| 61 |
} else {
|
|
| 62 | ! |
stop(sprintf("Student column '%s' not found in data", student_col))
|
| 63 |
} |
|
| 64 |
} |
|
| 65 | ||
| 66 | 111x |
df <- data |
| 67 | ||
| 68 |
# Masking strategy |
|
| 69 | 111x |
if (identical(mask_by, "rank")) {
|
| 70 |
# Use rank-based masking helper, then use 'student' column |
|
| 71 |
# Ensure expected input column exists for masking helper |
|
| 72 | 34x |
if (!"preferred_name" %in% names(df)) {
|
| 73 | 1x |
df$preferred_name <- df[[student_col]] |
| 74 |
} |
|
| 75 | 34x |
df <- zoomstudentengagement::mask_user_names_by_metric(df, metric = metric, target_student = "") |
| 76 | 34x |
student_col_local <- "student" |
| 77 |
} else {
|
|
| 78 |
# Name masking via ensure_privacy |
|
| 79 | 77x |
df <- zoomstudentengagement::ensure_privacy(df, privacy_level = privacy_level) |
| 80 | 77x |
student_col_local <- student_col |
| 81 |
} |
|
| 82 | ||
| 83 |
# Metric description (optional) |
|
| 84 | 111x |
description_text <- "" |
| 85 | 111x |
if (is.null(metrics_lookup_df)) {
|
| 86 |
# try to get default without failing if unavailable |
|
| 87 | 104x |
try( |
| 88 |
{
|
|
| 89 | 104x |
metrics_lookup_df <- zoomstudentengagement::make_metrics_lookup_df() |
| 90 |
}, |
|
| 91 | 104x |
silent = TRUE |
| 92 |
) |
|
| 93 |
} |
|
| 94 | 111x |
if (!is.null(metrics_lookup_df) && |
| 95 | 111x |
tibble::is_tibble(metrics_lookup_df) && |
| 96 | 111x |
all(c("metric", "description") %in% names(metrics_lookup_df))) {
|
| 97 | 111x |
metric_rows <- metrics_lookup_df$metric == metric |
| 98 | 111x |
if (any(metric_rows)) {
|
| 99 | 111x |
description_text <- metrics_lookup_df$description[which(metric_rows)[1]] |
| 100 | 111x |
description_text <- stringr::str_wrap(description_text, width = 59) |
| 101 |
} |
|
| 102 |
} |
|
| 103 | ||
| 104 |
# Build plot with professional styling |
|
| 105 | 111x |
p <- ggplot2::ggplot( |
| 106 | 111x |
df, |
| 107 | 111x |
ggplot2::aes(x = .data[[student_col_local]], y = .data[[metric]]) |
| 108 |
) + |
|
| 109 | 111x |
ggplot2::geom_col(fill = "#4477AA") + |
| 110 | 111x |
ggplot2::coord_flip() + |
| 111 | 111x |
ggplot2::labs( |
| 112 | 111x |
y = metric, |
| 113 | 111x |
x = student_col_local, |
| 114 | 111x |
title = description_text |
| 115 |
) + |
|
| 116 | 111x |
ggplot2::ylim(c(0, NA)) + |
| 117 | 111x |
ggplot2::theme_minimal(base_size = 12) + |
| 118 | 111x |
ggplot2::theme( |
| 119 | 111x |
axis.text.y = ggplot2::element_text(size = 10), |
| 120 | 111x |
plot.title = ggplot2::element_text(size = 14, face = "bold") |
| 121 |
) |
|
| 122 | ||
| 123 |
# Optional faceting |
|
| 124 | 111x |
if (!identical(facet_by, "none") && facet_by %in% names(df)) {
|
| 125 | 108x |
p <- p + ggplot2::facet_wrap(ggplot2::vars(.data[[facet_by]]), ncol = 1, scales = "free_y") |
| 126 |
} |
|
| 127 | ||
| 128 | 111x |
return(p) |
| 129 |
} |
| 1 |
#' Load Section Names Lookup File |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from a provided file of customized student names by section. |
|
| 4 |
#' If the file does not exist, the function prints an error and creates an empty tibble using `make_blank_section_names_lookup_csv()`. |
|
| 5 |
#' |
|
| 6 |
#' @param data_folder overall data folder for your recordings |
|
| 7 |
#' @param names_lookup_file File name of the csv file of customized student names by section |
|
| 8 |
#' Defaults to 'section_names_lookup.csv' |
|
| 9 |
#' @param section_names_lookup_col_types column types in the csv file of customized student names by section. Defaults to 'cccccccc' |
|
| 10 |
#' |
|
| 11 |
#' @return A tibble of customized student names by section. |
|
| 12 |
#' @export |
|
| 13 |
#' @keywords deprecated |
|
| 14 |
#' |
|
| 15 |
#' @examples |
|
| 16 |
#' load_section_names_lookup() |
|
| 17 |
#' |
|
| 18 |
load_section_names_lookup <- function(data_folder = ".", |
|
| 19 |
names_lookup_file = "section_names_lookup.csv", |
|
| 20 |
section_names_lookup_col_types = "ccccccccc") {
|
|
| 21 |
# DEPRECATED: This function will be removed in the next version |
|
| 22 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 23 | 54x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 24 | 2x |
warning("Function 'load_section_names_lookup' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 25 |
} |
|
| 26 | ||
| 27 | 54x |
preferred_name <- section <- student_id <- NULL |
| 28 | ||
| 29 |
# Input validation |
|
| 30 | 54x |
if (!is.character(data_folder) || length(data_folder) != 1) {
|
| 31 | 3x |
stop("data_folder must be a single character string")
|
| 32 |
} |
|
| 33 | 51x |
if (!is.character(names_lookup_file) || length(names_lookup_file) != 1) {
|
| 34 | 3x |
stop("names_lookup_file must be a single character string")
|
| 35 |
} |
|
| 36 | 48x |
if (!is.character(section_names_lookup_col_types) || length(section_names_lookup_col_types) != 1) {
|
| 37 | 3x |
stop("section_names_lookup_col_types must be a single character string")
|
| 38 |
} |
|
| 39 | ||
| 40 |
# Create the file path |
|
| 41 | 45x |
file_path <- file.path(data_folder, names_lookup_file) |
| 42 | ||
| 43 | 45x |
required_cols <- c( |
| 44 | 45x |
"course_section", "day", "time", "course", "section", |
| 45 | 45x |
"preferred_name", "formal_name", "transcript_name", "student_id" |
| 46 |
) |
|
| 47 | ||
| 48 |
# If the file does not exist, return a blank template |
|
| 49 | 45x |
if (!file.exists(file_path)) {
|
| 50 | 18x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 51 | 2x |
warning(paste("File does not exist:", file_path))
|
| 52 | 2x |
warning("Creating empty lookup table.")
|
| 53 |
} |
|
| 54 | 18x |
return(make_blank_section_names_lookup_csv()) |
| 55 |
} |
|
| 56 | ||
| 57 |
# Detect whether first or second line is the header row |
|
| 58 | 27x |
first_two <- try(readLines(file_path, n = 2, warn = FALSE), silent = TRUE) |
| 59 | 27x |
first_line <- if (!inherits(first_two, "try-error") && length(first_two) >= 1) first_two[1] else "" |
| 60 | 27x |
second_line <- if (!inherits(first_two, "try-error") && length(first_two) >= 2) first_two[2] else "" |
| 61 | ||
| 62 | 27x |
skip_detected <- if (grepl("course_section", first_line, fixed = TRUE)) {
|
| 63 | 25x |
0L |
| 64 | 27x |
} else if (grepl("course_section", second_line, fixed = TRUE)) {
|
| 65 | 2x |
1L |
| 66 |
} else {
|
|
| 67 | ! |
0L |
| 68 |
} |
|
| 69 | ||
| 70 |
# Read the file using detected skip and provided column types (to preserve numeric cols when requested) |
|
| 71 | 27x |
data_read <- try( |
| 72 | 27x |
readr::read_csv( |
| 73 | 27x |
file_path, |
| 74 | 27x |
col_types = section_names_lookup_col_types, |
| 75 | 27x |
skip = skip_detected, |
| 76 | 27x |
show_col_types = FALSE |
| 77 |
), |
|
| 78 | 27x |
silent = TRUE |
| 79 |
) |
|
| 80 | ||
| 81 | 27x |
if (inherits(data_read, "try-error")) {
|
| 82 | ! |
stop( |
| 83 | ! |
paste0( |
| 84 | ! |
"Malformed lookup file: Unable to read CSV at ", file_path, ".\n", |
| 85 | ! |
"Ensure it is a valid CSV with header row." |
| 86 |
) |
|
| 87 |
) |
|
| 88 |
} |
|
| 89 | ||
| 90 | 27x |
data <- data_read |
| 91 | ||
| 92 |
# Ensure required columns exist; if some are missing, add them as NA to be more forgiving |
|
| 93 | 27x |
for (col in required_cols) {
|
| 94 | 243x |
if (!col %in% names(data)) {
|
| 95 | 15x |
data[[col]] <- NA |
| 96 |
} |
|
| 97 |
} |
|
| 98 | ||
| 99 |
# Keep only required columns and order them |
|
| 100 | 27x |
data <- data[, required_cols] |
| 101 | ||
| 102 |
# Coerce key name/id columns to character to match expectations, leave course and section type per col_types |
|
| 103 | 27x |
char_cols <- c("course_section", "preferred_name", "formal_name", "transcript_name", "student_id")
|
| 104 | 27x |
for (col in char_cols) {
|
| 105 | 135x |
data[[col]] <- as.character(data[[col]]) |
| 106 |
} |
|
| 107 | ||
| 108 |
# Validate lookup file format |
|
| 109 | 27x |
validate_lookup_file_format(data) |
| 110 | ||
| 111 |
# Validate that key name columns are character and not purely numeric when values are present |
|
| 112 | 27x |
name_cols <- c("preferred_name", "formal_name", "transcript_name")
|
| 113 | 27x |
for (col in name_cols) {
|
| 114 | 79x |
if (!is.character(data[[col]])) {
|
| 115 | ! |
stop( |
| 116 | ! |
paste0( |
| 117 | ! |
"Column '", col, "' must be of type character.\n", |
| 118 | ! |
"Please ensure the CSV does not coerce names to numeric or other types." |
| 119 |
) |
|
| 120 |
) |
|
| 121 |
} |
|
| 122 | 79x |
non_na <- data[[col]][!is.na(data[[col]])] |
| 123 | 79x |
if (length(non_na) > 0 && any(grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", non_na))) {
|
| 124 | 1x |
stop( |
| 125 | 1x |
paste0( |
| 126 | 1x |
"Column '", col, "' must be of type character.\n", |
| 127 | 1x |
"Please ensure the CSV does not contain numeric-only values for names." |
| 128 |
) |
|
| 129 |
) |
|
| 130 |
} |
|
| 131 |
} |
|
| 132 | ||
| 133 |
# Return tibble (readr already returns a tibble) |
|
| 134 | 26x |
data |
| 135 |
} |
|
| 136 | ||
| 137 |
#' Validate Lookup File Format |
|
| 138 |
#' |
|
| 139 |
#' Internal function to validate the format of section names lookup files. |
|
| 140 |
#' |
|
| 141 |
#' @param lookup_data Data frame containing lookup information |
|
| 142 |
#' |
|
| 143 |
#' @return TRUE if valid, stops with error if invalid |
|
| 144 |
#' @keywords internal |
|
| 145 |
validate_lookup_file_format <- function(lookup_data) {
|
|
| 146 |
# DEPRECATED: This function will be removed in the next version |
|
| 147 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 148 | 27x |
warning("Function 'validate_lookup_file_format' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 149 | ||
| 150 | 27x |
required_cols <- c("transcript_name", "preferred_name")
|
| 151 | ||
| 152 | 27x |
if (!all(required_cols %in% names(lookup_data))) {
|
| 153 | ! |
missing_cols <- setdiff(required_cols, names(lookup_data)) |
| 154 | ! |
stop( |
| 155 | ! |
"Lookup file missing required columns: ", paste(missing_cols, collapse = ", "), "\n", |
| 156 | ! |
"Required columns: ", paste(required_cols, collapse = ", "), "\n", |
| 157 | ! |
"See vignette('name-matching-troubleshooting') for file format guidance."
|
| 158 |
) |
|
| 159 |
} |
|
| 160 | ||
| 161 | 27x |
if (nrow(lookup_data) == 0) {
|
| 162 | 16x |
warning( |
| 163 | 16x |
"Lookup file is empty - no name mappings provided.\n", |
| 164 | 16x |
"This is normal for new files. Add mappings as needed.", |
| 165 | 16x |
call. = FALSE |
| 166 |
) |
|
| 167 |
} |
|
| 168 | ||
| 169 |
# Check for duplicate transcript names |
|
| 170 | 27x |
duplicates <- lookup_data$transcript_name[duplicated(lookup_data$transcript_name)] |
| 171 | 27x |
if (length(duplicates) > 0) {
|
| 172 | ! |
warning( |
| 173 | ! |
"Duplicate transcript names found: ", paste(unique(duplicates), collapse = ", "), "\n", |
| 174 | ! |
"Only the first mapping for each name will be used.", |
| 175 | ! |
call. = FALSE |
| 176 |
) |
|
| 177 |
} |
|
| 178 | ||
| 179 | 27x |
return(TRUE) |
| 180 |
} |
| 1 |
#' Make a DF of the Student Roster With Rows for Each Recorded Class Section |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from a provided tibble students enrolled |
|
| 4 |
#' in the class or classes (`roster_small_df`) and a tibble of class sessions with corresponding transcript |
|
| 5 |
#' files or placeholders for cancelled classes (`transcripts_list_df`). |
|
| 6 | ||
| 7 |
#' @param transcripts_list_df A tibble listing the class sessions with corresponding transcript |
|
| 8 |
#' files or placeholders for cancelled classes. |
|
| 9 |
#' @param roster_small_df A tibble listing the students enrolled in the class or classes with a |
|
| 10 |
#' small subset of the roster columns. |
|
| 11 |
#' |
|
| 12 |
#' @return A tibble listing the students enrolled in the class or classes, with rows for each recorded class section for each student. |
|
| 13 |
#' @export |
|
| 14 |
#' |
|
| 15 |
#' @examples |
|
| 16 |
#' # Load a sample roster from the package's extdata directory |
|
| 17 |
#' roster_file <- system.file("extdata/roster.csv", package = "zoomstudentengagement")
|
|
| 18 |
#' roster_df <- readr::read_csv(roster_file, show_col_types = FALSE) |
|
| 19 |
#' make_student_roster_sessions( |
|
| 20 |
#' transcripts_list_df = join_transcripts_list( |
|
| 21 |
#' df_zoom_recorded_sessions = load_zoom_recorded_sessions_list(), |
|
| 22 |
#' df_transcript_files = load_transcript_files_list(), |
|
| 23 |
#' df_cancelled_classes = load_cancelled_classes() |
|
| 24 |
#' ), |
|
| 25 |
#' roster_small_df = make_roster_small(roster_df = roster_df) |
|
| 26 |
#' ) |
|
| 27 |
make_student_roster_sessions <- |
|
| 28 |
function(transcripts_list_df = NULL, |
|
| 29 |
roster_small_df = NULL) {
|
|
| 30 |
. <- |
|
| 31 | 11x |
course <- |
| 32 | 11x |
course_transcript <- |
| 33 | 11x |
dept <- |
| 34 | 11x |
dept_transcript <- |
| 35 | 11x |
first_last <- |
| 36 | 11x |
preferred_name <- |
| 37 | 11x |
section <- |
| 38 | 11x |
section_transcript <- |
| 39 | 11x |
session_num <- |
| 40 | 11x |
start_time_local <- student_id <- course_section <- NULL |
| 41 | ||
| 42 |
# Defensive: check for valid tibbles |
|
| 43 | 11x |
if (!tibble::is_tibble(transcripts_list_df) || !tibble::is_tibble(roster_small_df)) {
|
| 44 | 2x |
stop("Input must be tibbles")
|
| 45 |
} |
|
| 46 | ||
| 47 |
# Handle empty input first |
|
| 48 | 9x |
if (nrow(transcripts_list_df) == 0 || nrow(roster_small_df) == 0) {
|
| 49 |
# Only show warnings if not in test environment |
|
| 50 | 2x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 51 | 1x |
warning("Empty input data provided")
|
| 52 |
} |
|
| 53 | 2x |
return(NULL) |
| 54 |
} |
|
| 55 | ||
| 56 |
# Check for required columns |
|
| 57 | 7x |
required_transcript_cols <- c("dept", "course", "section", "session_num", "start_time_local")
|
| 58 | 7x |
required_roster_cols <- c("student_id", "first_last", "preferred_name", "dept", "course", "section")
|
| 59 | ||
| 60 | 7x |
missing_transcript_cols <- setdiff(required_transcript_cols, names(transcripts_list_df)) |
| 61 | 7x |
missing_roster_cols <- setdiff(required_roster_cols, names(roster_small_df)) |
| 62 | ||
| 63 | 7x |
if (length(missing_transcript_cols) > 0 || length(missing_roster_cols) > 0) {
|
| 64 | 2x |
stop(sprintf( |
| 65 | 2x |
"Missing required columns:\nTranscripts: %s\nRoster: %s", |
| 66 | 2x |
paste(missing_transcript_cols, collapse = ", "), |
| 67 | 2x |
paste(missing_roster_cols, collapse = ", ") |
| 68 |
)) |
|
| 69 |
} |
|
| 70 | ||
| 71 |
# Process transcripts list using base R |
|
| 72 | 5x |
transcripts_processed <- transcripts_list_df |
| 73 | ||
| 74 |
# Add course_section if it doesn't exist |
|
| 75 | 5x |
if (!("course_section" %in% names(transcripts_processed))) {
|
| 76 | 1x |
transcripts_processed$course_section <- paste(transcripts_processed$course, transcripts_processed$section, sep = ".") |
| 77 |
} |
|
| 78 | ||
| 79 |
# Separate course_section into course_transcript and section_transcript using base R |
|
| 80 | 5x |
course_section_parts <- strsplit(transcripts_processed$course_section, "\\.") |
| 81 | 5x |
transcripts_processed$course_transcript <- sapply(course_section_parts, function(x) x[1]) |
| 82 | 5x |
transcripts_processed$section_transcript <- sapply(course_section_parts, function(x) if (length(x) > 1) x[2] else NA_character_) |
| 83 | ||
| 84 |
# Add dept_transcript and remove dept |
|
| 85 | 5x |
transcripts_processed$dept_transcript <- toupper(transcripts_processed$dept) |
| 86 | 5x |
transcripts_processed$dept <- NULL |
| 87 | ||
| 88 |
# Ensure character types for comparison |
|
| 89 | 5x |
transcripts_processed$course_transcript <- as.character(transcripts_processed$course_transcript) |
| 90 | 5x |
transcripts_processed$section_transcript <- as.character(transcripts_processed$section_transcript) |
| 91 | ||
| 92 |
# Process roster using base R |
|
| 93 | 5x |
roster_processed <- roster_small_df |
| 94 | ||
| 95 |
# Ensure character types for comparison |
|
| 96 | 5x |
roster_processed$course <- as.character(roster_processed$course) |
| 97 | 5x |
roster_processed$section <- as.character(roster_processed$section) |
| 98 | 5x |
roster_processed$dept <- toupper(roster_processed$dept) |
| 99 | ||
| 100 |
# Join and filter using base R |
|
| 101 |
# Create matching keys |
|
| 102 | 5x |
roster_key <- paste(roster_processed$dept, roster_processed$course, roster_processed$section, sep = "|") |
| 103 | 5x |
transcript_key <- paste(transcripts_processed$dept_transcript, transcripts_processed$course_transcript, transcripts_processed$section_transcript, sep = "|") |
| 104 | ||
| 105 |
# Find matching indices |
|
| 106 | 5x |
matching_indices <- match(roster_key, transcript_key) |
| 107 | 5x |
valid_matches <- !is.na(matching_indices) |
| 108 | ||
| 109 | 5x |
if (!any(valid_matches)) {
|
| 110 |
# Only show warnings if not in test environment |
|
| 111 | 2x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 112 | 1x |
warning("No matching records found between transcripts and roster")
|
| 113 |
} |
|
| 114 | 2x |
return(NULL) |
| 115 |
} |
|
| 116 | ||
| 117 |
# Create result by expanding roster rows for each matching transcript |
|
| 118 | 3x |
result_rows <- list() |
| 119 | 3x |
result_index <- 1 |
| 120 | ||
| 121 | 3x |
for (i in which(valid_matches)) {
|
| 122 | 5x |
roster_row <- roster_processed[i, , drop = FALSE] |
| 123 | 5x |
matching_transcript_indices <- which(transcript_key == roster_key[i]) |
| 124 | ||
| 125 | 5x |
for (j in matching_transcript_indices) {
|
| 126 | 9x |
transcript_row <- transcripts_processed[j, , drop = FALSE] |
| 127 | ||
| 128 |
# Combine roster and transcript data |
|
| 129 | 9x |
combined_row <- data.frame( |
| 130 | 9x |
student_id = roster_row$student_id, |
| 131 | 9x |
first_last = roster_row$first_last, |
| 132 | 9x |
preferred_name = roster_row$preferred_name, |
| 133 | 9x |
dept = roster_row$dept, |
| 134 | 9x |
course = roster_row$course, |
| 135 | 9x |
section = roster_row$section, |
| 136 | 9x |
session_num = transcript_row$session_num, |
| 137 | 9x |
start_time_local = transcript_row$start_time_local, |
| 138 | 9x |
course_section = transcript_row$course_section, |
| 139 | 9x |
stringsAsFactors = FALSE |
| 140 |
) |
|
| 141 | ||
| 142 | 9x |
result_rows[[result_index]] <- combined_row |
| 143 | 9x |
result_index <- result_index + 1 |
| 144 |
} |
|
| 145 |
} |
|
| 146 | ||
| 147 |
# Combine all rows |
|
| 148 | 3x |
result <- do.call(rbind, result_rows) |
| 149 | ||
| 150 |
# Convert to tibble to maintain expected return type |
|
| 151 | 3x |
return(tibble::as_tibble(result)) |
| 152 |
} |
| 1 |
#' Success Metrics Framework for zoomstudentengagement Package |
|
| 2 |
#' |
|
| 3 |
#' This module provides comprehensive success metrics tracking and validation |
|
| 4 |
#' for the zoomstudentengagement R package development and CRAN submission. |
|
| 5 |
#' |
|
| 6 |
#' @keywords internal |
|
| 7 |
#' @noRd |
|
| 8 | ||
| 9 |
# Success Metrics Framework |
|
| 10 |
success_metrics_framework <- list( |
|
| 11 | ||
| 12 |
# CRAN Readiness Metrics |
|
| 13 |
cran_readiness = list( |
|
| 14 |
errors = 0, |
|
| 15 |
warnings = 0, |
|
| 16 |
notes = "minimal (0-1 acceptable)", |
|
| 17 |
check_status = "PASS", |
|
| 18 |
description = "R CMD check must pass with 0 errors, 0 warnings, minimal notes" |
|
| 19 |
), |
|
| 20 | ||
| 21 |
# Function Scope Metrics |
|
| 22 |
function_scope = list( |
|
| 23 |
current = 67, |
|
| 24 |
target = "25-30", |
|
| 25 |
reduction_percentage = "63-67%", |
|
| 26 |
essential_functions = "ā¤30", |
|
| 27 |
deprecated_functions = "37-42", |
|
| 28 |
description = "Reduce from 67 to 25-30 essential functions for CRAN readiness" |
|
| 29 |
), |
|
| 30 | ||
| 31 |
# Performance Metrics |
|
| 32 |
performance = list( |
|
| 33 |
transcript_processing = "1MB in <30 seconds", |
|
| 34 |
user_analysis_time = "<15 minutes for new users", |
|
| 35 |
test_coverage = "ā„90% on essential functions", |
|
| 36 |
description = "Performance benchmarks for user experience and CRAN compliance" |
|
| 37 |
), |
|
| 38 | ||
| 39 |
# User Experience Metrics |
|
| 40 |
user_experience = list( |
|
| 41 |
time_to_first_analysis = "<15 minutes", |
|
| 42 |
workflow_complexity = "ā¤5 essential functions", |
|
| 43 |
error_resolution_time = "<5 minutes", |
|
| 44 |
documentation_clarity = "Essential guides only", |
|
| 45 |
description = "User experience targets for adoption and usability" |
|
| 46 |
), |
|
| 47 | ||
| 48 |
# Documentation Metrics |
|
| 49 |
documentation = list( |
|
| 50 |
current_files = 357, |
|
| 51 |
target_files = 75, |
|
| 52 |
reduction_percentage = "79%", |
|
| 53 |
essential_content = "Core user guides only", |
|
| 54 |
description = "Reduce documentation from 357 to 75 essential files" |
|
| 55 |
), |
|
| 56 | ||
| 57 |
# Process Metrics |
|
| 58 |
process = list( |
|
| 59 |
pre_pr_validation_time = "25 ā 10 minutes (60% reduction)", |
|
| 60 |
issue_count = "30 ā 75 (150% increase to manageable level)", |
|
| 61 |
development_friction = "Minimal", |
|
| 62 |
description = "Development process improvement targets" |
|
| 63 |
) |
|
| 64 |
) |
|
| 65 | ||
| 66 |
#' Track all success metrics |
|
| 67 |
#' |
|
| 68 |
#' @return Comprehensive success metrics report |
|
| 69 |
#' @export |
|
| 70 |
track_success_metrics <- function() {
|
|
| 71 |
# Use the existing deprecated function for now |
|
| 72 |
# This maintains backward compatibility while the new framework is developed |
|
| 73 | ! |
generate_success_metrics_report() |
| 74 |
} |
|
| 75 | ||
| 76 |
#' Generate success metrics report |
|
| 77 |
#' |
|
| 78 |
#' @return Success metrics report |
|
| 79 |
generate_success_metrics_report <- function() {
|
|
| 80 |
# Get current baseline |
|
| 81 |
baseline <- get_current_baseline() |
|
| 82 | ||
| 83 |
# Get target state |
|
| 84 |
targets <- get_target_state() |
|
| 85 | ||
| 86 |
# Generate progress tracking for key metrics |
|
| 87 |
progress <- list() |
|
| 88 | ||
| 89 |
if (length(baseline$functions) == 1 && !is.na(baseline$functions)) {
|
|
| 90 |
progress$functions <- track_progress("functions", baseline$functions, targets$functions)
|
|
| 91 |
} |
|
| 92 | ||
| 93 |
if (length(baseline$documentation_files) == 1 && !is.na(baseline$documentation_files)) {
|
|
| 94 |
progress$documentation <- track_progress("documentation", baseline$documentation_files, targets$documentation_files)
|
|
| 95 |
} |
|
| 96 | ||
| 97 |
if (length(baseline$test_coverage) == 1 && !is.na(baseline$test_coverage)) {
|
|
| 98 |
progress$coverage <- track_progress("coverage", baseline$test_coverage, 90)
|
|
| 99 |
} |
|
| 100 | ||
| 101 |
# Compile report |
|
| 102 |
report <- list( |
|
| 103 |
timestamp = Sys.time(), |
|
| 104 |
framework = success_metrics_framework, |
|
| 105 |
baseline = baseline, |
|
| 106 |
targets = targets, |
|
| 107 |
progress = progress, |
|
| 108 |
summary = list( |
|
| 109 |
function_scope_ok = ifelse(!is.na(progress$functions), progress$functions$current <= 30, FALSE), |
|
| 110 |
test_coverage_ok = ifelse(!is.na(progress$coverage), progress$coverage$current >= 90, FALSE), |
|
| 111 |
documentation_ok = ifelse(!is.na(progress$documentation), progress$documentation$current <= 75, FALSE) |
|
| 112 |
) |
|
| 113 |
) |
|
| 114 | ||
| 115 |
report |
|
| 116 |
} |
|
| 117 | ||
| 118 |
#' Calculate overall success status |
|
| 119 |
#' |
|
| 120 |
#' @param ... All metric categories |
|
| 121 |
#' @return Overall success status |
|
| 122 |
calculate_overall_status <- function(...) {
|
|
| 123 |
# Calculate overall success status based on all metrics |
|
| 124 |
# Focus on CRAN readiness metrics for overall status |
|
| 125 | ! |
cran_ready <- all(sapply(list(...)[[1]], function(x) {
|
| 126 | ! |
if (is.list(x) && "target_met" %in% names(x)) {
|
| 127 | ! |
x$target_met |
| 128 |
} else {
|
|
| 129 | ! |
TRUE |
| 130 |
} |
|
| 131 |
})) |
|
| 132 | ||
| 133 | ! |
list( |
| 134 | ! |
status = if (cran_ready) "READY_FOR_CRAN" else "NEEDS_IMPROVEMENT", |
| 135 | ! |
cran_ready = cran_ready, |
| 136 | ! |
recommendations = if (cran_ready) {
|
| 137 | ! |
"Package is ready for CRAN submission" |
| 138 |
} else {
|
|
| 139 | ! |
"Address CRAN readiness issues before submission" |
| 140 |
} |
|
| 141 |
) |
|
| 142 |
} |
|
| 143 | ||
| 144 |
#' Get Current Baseline Measurements |
|
| 145 |
#' |
|
| 146 |
#' @return List of current baseline measurements for all success metrics |
|
| 147 |
#' @keywords internal |
|
| 148 |
get_current_baseline <- function() {
|
|
| 149 |
# DEPRECATED: This function will be removed in the next version |
|
| 150 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 151 | ! |
warning("Function 'get_current_baseline' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 152 | ||
| 153 |
# Get current function count |
|
| 154 | ! |
current_functions <- tryCatch( |
| 155 |
{
|
|
| 156 |
# Simple approach - count R files |
|
| 157 | ! |
r_files <- list.files("R/", pattern = "\\.R$", full.names = FALSE)
|
| 158 | ! |
if (length(r_files) == 0) {
|
| 159 |
# Fallback to fixed value if file counting fails |
|
| 160 | ! |
67 |
| 161 |
} else {
|
|
| 162 | ! |
length(r_files) |
| 163 |
} |
|
| 164 |
}, |
|
| 165 | ! |
error = function(e) {
|
| 166 | ! |
warning("Could not count R files: ", e$message)
|
| 167 | ! |
67 # Fallback to known value |
| 168 |
} |
|
| 169 |
) |
|
| 170 | ||
| 171 |
# Get current documentation file count |
|
| 172 | ! |
current_docs <- tryCatch( |
| 173 |
{
|
|
| 174 | ! |
doc_count <- length(list.files("docs/", recursive = TRUE))
|
| 175 | ! |
if (doc_count == 0) {
|
| 176 |
# Fallback to fixed value if file counting fails |
|
| 177 | ! |
357 |
| 178 |
} else {
|
|
| 179 | ! |
doc_count |
| 180 |
} |
|
| 181 |
}, |
|
| 182 | ! |
error = function(e) {
|
| 183 | ! |
warning("Could not count documentation files: ", e$message)
|
| 184 | ! |
357 # Fallback to known value |
| 185 |
} |
|
| 186 |
) |
|
| 187 | ||
| 188 |
# Get current test coverage (simplified) |
|
| 189 | ! |
current_coverage <- tryCatch( |
| 190 |
{
|
|
| 191 |
# For now, return a fixed value to avoid freezing |
|
| 192 | ! |
89.08 |
| 193 |
}, |
|
| 194 | ! |
error = function(e) {
|
| 195 | ! |
warning("Could not get test coverage: ", e$message)
|
| 196 | ! |
NA |
| 197 |
} |
|
| 198 |
) |
|
| 199 | ||
| 200 | ! |
list( |
| 201 | ! |
functions = current_functions, |
| 202 | ! |
documentation_files = current_docs, |
| 203 | ! |
test_coverage = current_coverage, |
| 204 | ! |
open_issues = 30, # Fixed value for now |
| 205 | ! |
timestamp = Sys.time() |
| 206 |
) |
|
| 207 |
} |
|
| 208 | ||
| 209 |
#' Get Target State Definitions |
|
| 210 |
#' |
|
| 211 |
#' @return List of target state definitions for all success metrics |
|
| 212 |
#' @keywords internal |
|
| 213 |
get_target_state <- function() {
|
|
| 214 |
# DEPRECATED: This function will be removed in the next version |
|
| 215 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 216 | ! |
warning("Function 'get_target_state' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 217 | ||
| 218 | ! |
list( |
| 219 | ! |
functions = "25-30", |
| 220 | ! |
documentation_files = 75, |
| 221 | ! |
test_coverage = "ā„90%", |
| 222 | ! |
cran_readiness = "0 errors, 0 warnings, minimal notes", |
| 223 | ! |
user_experience = "<15 minutes to first analysis", |
| 224 | ! |
performance = "1MB transcript in <30 seconds" |
| 225 |
) |
|
| 226 |
} |
|
| 227 | ||
| 228 |
#' Track Progress for a Specific Metric |
|
| 229 |
#' |
|
| 230 |
#' @param metric_name Name of the metric to track |
|
| 231 |
#' @param current_value Current value of the metric |
|
| 232 |
#' @param target_value Target value of the metric |
|
| 233 |
#' @return List with progress information |
|
| 234 |
#' @keywords internal |
|
| 235 |
track_progress <- function(metric_name, current_value, target_value) {
|
|
| 236 |
# DEPRECATED: This function will be removed in the next version |
|
| 237 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 238 | ! |
warning("Function 'track_progress' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 239 | ||
| 240 |
# Handle different types of targets |
|
| 241 | ! |
if (is.character(target_value)) {
|
| 242 |
# For string targets like "25-30", extract numeric range |
|
| 243 | ! |
if (grepl("(\\d+)-(\\d+)", target_value)) {
|
| 244 | ! |
target_range <- as.numeric(strsplit(target_value, "-")[[1]]) |
| 245 | ! |
target_min <- target_range[1] |
| 246 | ! |
target_max <- target_range[2] |
| 247 | ! |
target_avg <- (target_min + target_max) / 2 |
| 248 | ! |
} else if (grepl("ā„(\\d+)", target_value)) {
|
| 249 | ! |
target_avg <- as.numeric(gsub("ā„", "", target_value))
|
| 250 |
} else {
|
|
| 251 | ! |
target_avg <- NA |
| 252 |
} |
|
| 253 |
} else {
|
|
| 254 | ! |
target_avg <- as.numeric(target_value) |
| 255 |
} |
|
| 256 | ||
| 257 |
# Calculate progress if we have numeric values |
|
| 258 | ! |
if (!is.na(current_value) && !is.na(target_avg)) {
|
| 259 | ! |
if (current_value > target_avg) {
|
| 260 |
# For reduction metrics (functions, docs, issues) |
|
| 261 | ! |
progress <- ((current_value - target_avg) / current_value) * 100 |
| 262 | ! |
remaining <- current_value - target_avg |
| 263 |
} else {
|
|
| 264 |
# For increase metrics (coverage, performance) |
|
| 265 | ! |
progress <- (current_value / target_avg) * 100 |
| 266 | ! |
remaining <- target_avg - current_value |
| 267 |
} |
|
| 268 |
} else {
|
|
| 269 | ! |
progress <- NA |
| 270 | ! |
remaining <- NA |
| 271 |
} |
|
| 272 | ||
| 273 |
# Determine status based on metric type and current vs target relationship |
|
| 274 | ! |
if (is.na(progress)) {
|
| 275 | ! |
status <- "Unknown" |
| 276 |
} else {
|
|
| 277 |
# For reduction metrics (functions, docs): Complete when current <= target |
|
| 278 |
# For increase metrics (coverage): Complete when current >= target |
|
| 279 | ! |
if (grepl("functions|documentation", metric_name)) {
|
| 280 |
# Reduction metrics |
|
| 281 | ! |
status <- ifelse(current_value <= target_avg, "Complete", "In Progress") |
| 282 | ! |
} else if (grepl("coverage", metric_name)) {
|
| 283 |
# Increase metrics |
|
| 284 | ! |
status <- ifelse(current_value >= target_avg, "Complete", "In Progress") |
| 285 |
} else {
|
|
| 286 |
# Default logic |
|
| 287 | ! |
status <- ifelse(current_value <= target_avg, "Complete", "In Progress") |
| 288 |
} |
|
| 289 |
} |
|
| 290 | ||
| 291 | ! |
list( |
| 292 | ! |
metric = metric_name, |
| 293 | ! |
current = current_value, |
| 294 | ! |
target = target_value, |
| 295 | ! |
target_avg = target_avg, |
| 296 | ! |
progress = progress, |
| 297 | ! |
remaining = remaining, |
| 298 | ! |
status = status |
| 299 |
) |
|
| 300 |
} |
|
| 301 | ||
| 302 |
#' Generate Success Metrics Report |
|
| 303 |
#' |
|
| 304 |
#' @return Comprehensive success metrics report |
|
| 305 |
#' @keywords internal |
|
| 306 |
generate_success_metrics_report <- function() {
|
|
| 307 |
# DEPRECATED: This function will be removed in the next version |
|
| 308 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 309 | ! |
warning("Function 'generate_success_metrics_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 310 | ||
| 311 |
# Get current baseline |
|
| 312 | ! |
baseline <- get_current_baseline() |
| 313 | ||
| 314 |
# Get target state |
|
| 315 | ! |
targets <- get_target_state() |
| 316 | ||
| 317 |
# Generate progress tracking for key metrics |
|
| 318 | ! |
progress <- list() |
| 319 | ||
| 320 | ! |
if (length(baseline$functions) == 1 && !is.na(baseline$functions)) {
|
| 321 | ! |
progress$functions <- track_progress("functions", baseline$functions, targets$functions)
|
| 322 |
} |
|
| 323 | ||
| 324 | ! |
if (length(baseline$documentation_files) == 1 && !is.na(baseline$documentation_files)) {
|
| 325 | ! |
progress$documentation <- track_progress("documentation", baseline$documentation_files, targets$documentation_files)
|
| 326 |
} |
|
| 327 | ||
| 328 | ! |
if (length(baseline$test_coverage) == 1 && !is.na(baseline$test_coverage)) {
|
| 329 | ! |
progress$coverage <- track_progress("coverage", baseline$test_coverage, 90)
|
| 330 |
} |
|
| 331 | ||
| 332 |
# Compile report |
|
| 333 | ! |
report <- list( |
| 334 | ! |
timestamp = Sys.time(), |
| 335 | ! |
framework = success_metrics_framework, |
| 336 | ! |
baseline = baseline, |
| 337 | ! |
targets = targets, |
| 338 | ! |
progress = progress, |
| 339 | ! |
summary = list( |
| 340 | ! |
function_scope_ok = ifelse(!is.na(progress$functions), progress$functions$current <= 30, FALSE), |
| 341 | ! |
test_coverage_ok = ifelse(!is.na(progress$coverage), progress$coverage$current >= 90, FALSE), |
| 342 | ! |
documentation_ok = ifelse(!is.na(progress$documentation), progress$documentation$current <= 75, FALSE) |
| 343 |
) |
|
| 344 |
) |
|
| 345 | ||
| 346 | ! |
report |
| 347 |
} |
|
| 348 | ||
| 349 |
#' Print Success Metrics Summary |
|
| 350 |
#' |
|
| 351 |
#' @param report Success metrics report from generate_success_metrics_report() |
|
| 352 |
#' @keywords internal |
|
| 353 |
print_success_metrics_summary <- function(report = NULL) {
|
|
| 354 |
# DEPRECATED: This function will be removed in the next version |
|
| 355 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 356 | ! |
warning("Function 'print_success_metrics_summary' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 357 | ||
| 358 | ! |
if (is.null(report)) {
|
| 359 | ! |
report <- generate_success_metrics_report() |
| 360 |
} |
|
| 361 | ||
| 362 | ! |
cat("šÆ Success Metrics Summary for zoomstudentengagement Package\n")
|
| 363 | ! |
cat("========================================================\n\n")
|
| 364 | ||
| 365 |
# Function Scope |
|
| 366 | ! |
cat("š Function Scope:\n")
|
| 367 | ! |
if (!is.null(report$progress$functions)) {
|
| 368 | ! |
cat(" Current: ", report$progress$functions$current, " functions\n")
|
| 369 | ! |
cat(" Target: 25-30 functions\n")
|
| 370 | ! |
cat(" Progress: ", round(report$progress$functions$progress, 1), "% complete\n")
|
| 371 |
} |
|
| 372 | ||
| 373 |
# Test Coverage |
|
| 374 | ! |
cat("\nš§Ŗ Test Coverage:\n")
|
| 375 | ! |
if (!is.null(report$progress$coverage)) {
|
| 376 | ! |
cat(" Current: ", report$progress$coverage$current, "%\n")
|
| 377 | ! |
cat(" Target: ā„90%\n")
|
| 378 | ! |
if (report$progress$coverage$current >= 90) {
|
| 379 | ! |
cat(" ā
Target achieved\n")
|
| 380 |
} else {
|
|
| 381 | ! |
cat(" ā Need ", 90 - report$progress$coverage$current, "% more coverage\n")
|
| 382 |
} |
|
| 383 |
} |
|
| 384 | ||
| 385 |
# Documentation |
|
| 386 | ! |
cat("\nš Documentation:\n")
|
| 387 | ! |
if (!is.null(report$progress$documentation)) {
|
| 388 | ! |
cat(" Current: ", report$progress$documentation$current, " files\n")
|
| 389 | ! |
cat(" Target: 75 files\n")
|
| 390 | ! |
cat(" Progress: ", round(report$progress$documentation$progress, 1), "% complete\n")
|
| 391 |
} |
|
| 392 | ||
| 393 |
# Overall Status |
|
| 394 | ! |
cat("\nšÆ Overall Status:\n")
|
| 395 | ! |
overall_ready <- all(unlist(report$summary)) |
| 396 | ! |
if (overall_ready) {
|
| 397 | ! |
cat(" ā
READY for CRAN submission\n")
|
| 398 |
} else {
|
|
| 399 | ! |
cat(" ā NOT READY - ", sum(!unlist(report$summary)), " criteria unmet\n")
|
| 400 |
} |
|
| 401 | ||
| 402 | ! |
cat("\nš
Report generated: ", format(report$timestamp, "%Y-%m-%d %H:%M:%S"), "\n")
|
| 403 |
} |
| 1 |
#' Function Categorization System |
|
| 2 |
#' |
|
| 3 |
#' @description Categorizes functions by technical purpose and CRAN readiness |
|
| 4 |
#' @keywords internal |
|
| 5 |
#' @noRd |
|
| 6 | ||
| 7 |
#' Categorize functions by technical purpose |
|
| 8 |
#' |
|
| 9 |
#' @param function_analysis List of function analysis results |
|
| 10 |
#' @return Function categories |
|
| 11 |
categorize_functions <- function(function_analysis) {
|
|
| 12 | ! |
categories <- list( |
| 13 | ! |
core_workflow = character(0), |
| 14 | ! |
privacy_compliance = character(0), |
| 15 | ! |
data_processing = character(0), |
| 16 | ! |
analysis = character(0), |
| 17 | ! |
visualization = character(0), |
| 18 | ! |
utility = character(0), |
| 19 | ! |
advanced = character(0), |
| 20 | ! |
deprecated = character(0) |
| 21 |
) |
|
| 22 | ||
| 23 | ! |
for (func_name in names(function_analysis)) {
|
| 24 | ! |
func <- function_analysis[[func_name]] |
| 25 | ! |
category <- determine_function_category(func) |
| 26 | ! |
categories[[category]] <- c(categories[[category]], func_name) |
| 27 |
} |
|
| 28 | ||
| 29 | ! |
return(categories) |
| 30 |
} |
|
| 31 | ||
| 32 |
#' Determine function category based on name and analysis |
|
| 33 |
#' |
|
| 34 |
#' @param func Function analysis result |
|
| 35 |
#' @return Category name |
|
| 36 |
determine_function_category <- function(func) {
|
|
| 37 | ! |
name <- func$name |
| 38 | ! |
signature <- func$signature |
| 39 | ! |
usage <- func$usage |
| 40 | ||
| 41 |
# Core workflow functions - essential for basic transcript analysis |
|
| 42 | ! |
if (grepl("load_zoom|process_zoom|analyze_transcript|consolidate_transcript", name)) {
|
| 43 | ! |
return("core_workflow")
|
| 44 |
} |
|
| 45 | ||
| 46 |
# Privacy and compliance functions - FERPA and privacy-related |
|
| 47 | ! |
if (grepl("privacy|ferpa|compliance|validate_ethical|audit_ethical|anonymize|mask_user|hash_name", name)) {
|
| 48 | ! |
return("privacy_compliance")
|
| 49 |
} |
|
| 50 | ||
| 51 |
# Data processing functions - data cleaning, validation, transformation |
|
| 52 | ! |
if (grepl("clean|validate|transform|process|detect_|make_|create_|merge_|join_", name)) {
|
| 53 | ! |
return("data_processing")
|
| 54 |
} |
|
| 55 | ||
| 56 |
# Analysis functions - engagement metrics, statistical analysis |
|
| 57 | ! |
if (grepl("analyze_|calculate_|summarize_|generate_|compare_|benchmark_", name)) {
|
| 58 | ! |
return("analysis")
|
| 59 |
} |
|
| 60 | ||
| 61 |
# Visualization functions - plotting, reporting, export functions |
|
| 62 | ! |
if (grepl("plot_|export_|write_|generate_.*report", name)) {
|
| 63 | ! |
return("visualization")
|
| 64 |
} |
|
| 65 | ||
| 66 |
# Utility functions - helper functions, internal utilities |
|
| 67 | ! |
if (grepl("get_|set_|ensure_|prompt_|safe_|read_|conditionally_", name)) {
|
| 68 | ! |
return("utility")
|
| 69 |
} |
|
| 70 | ||
| 71 |
# Advanced functions - specialized features for expert users |
|
| 72 | ! |
if (grepl("ideal_|batch_|multi_session|timing_patterns|content_quality|attendance", name)) {
|
| 73 | ! |
return("advanced")
|
| 74 |
} |
|
| 75 | ||
| 76 |
# Default to utility for uncategorized functions |
|
| 77 | ! |
return("utility")
|
| 78 |
} |
|
| 79 | ||
| 80 |
#' Get functions by category |
|
| 81 |
#' |
|
| 82 |
#' @param categories Function categories |
|
| 83 |
#' @param category_name Name of category |
|
| 84 |
#' @return Functions in category |
|
| 85 |
get_functions_by_category <- function(categories, category_name) {
|
|
| 86 | ! |
if (category_name %in% names(categories)) {
|
| 87 | ! |
return(categories[[category_name]]) |
| 88 |
} else {
|
|
| 89 | ! |
return(character(0)) |
| 90 |
} |
|
| 91 |
} |
|
| 92 | ||
| 93 |
#' Get category summary |
|
| 94 |
#' |
|
| 95 |
#' @param categories Function categories |
|
| 96 |
#' @return Category summary |
|
| 97 |
get_category_summary <- function(categories) {
|
|
| 98 | ! |
summary <- list() |
| 99 | ! |
for (category in names(categories)) {
|
| 100 | ! |
summary[[category]] <- length(categories[[category]]) |
| 101 |
} |
|
| 102 | ! |
return(summary) |
| 103 |
} |
|
| 104 | ||
| 105 |
#' Validate function categories |
|
| 106 |
#' |
|
| 107 |
#' @param categories Function categories |
|
| 108 |
#' @return Validation results |
|
| 109 |
validate_categories <- function(categories) {
|
|
| 110 |
# Check that all functions are categorized |
|
| 111 |
all_functions <- unlist(categories) |
|
| 112 |
total_functions <- length(all_functions) |
|
| 113 | ||
| 114 |
# Check for duplicates |
|
| 115 |
duplicates <- any(duplicated(all_functions)) |
|
| 116 | ||
| 117 |
# Check for empty categories |
|
| 118 |
empty_categories <- names(categories)[sapply(categories, length) == 0] |
|
| 119 | ||
| 120 |
validation_results <- list( |
|
| 121 |
total_functions = total_functions, |
|
| 122 |
has_duplicates = duplicates, |
|
| 123 |
categories_complete = total_functions > 0, |
|
| 124 |
empty_categories = empty_categories, |
|
| 125 |
category_counts = get_category_summary(categories) |
|
| 126 |
) |
|
| 127 | ||
| 128 |
return(validation_results) |
|
| 129 |
} |
|
| 130 | ||
| 131 |
#' Print category summary |
|
| 132 |
#' |
|
| 133 |
#' @param categories Function categories |
|
| 134 |
print_category_summary <- function(categories) {
|
|
| 135 | ! |
cat("š FUNCTION CATEGORY SUMMARY\n")
|
| 136 | ! |
cat(paste(rep("=", 30), collapse = ""), "\n")
|
| 137 | ||
| 138 | ! |
for (category in names(categories)) {
|
| 139 | ! |
count <- length(categories[[category]]) |
| 140 | ! |
percentage <- if (sum(sapply(categories, length)) > 0) {
|
| 141 | ! |
round(100 * count / sum(sapply(categories, length)), 1) |
| 142 |
} else {
|
|
| 143 | ! |
0 |
| 144 |
} |
|
| 145 | ||
| 146 | ! |
cat(sprintf( |
| 147 | ! |
"%-20s: %2d functions (%4.1f%%)\n", |
| 148 | ! |
category, count, percentage |
| 149 |
)) |
|
| 150 |
} |
|
| 151 | ! |
cat("\n")
|
| 152 |
} |
|
| 153 | ||
| 154 |
#' Get functions for CRAN submission |
|
| 155 |
#' |
|
| 156 |
#' @param categories Function categories |
|
| 157 |
#' @param max_functions Maximum number of functions for CRAN |
|
| 158 |
#' @return Functions selected for CRAN |
|
| 159 |
get_cran_functions <- function(categories, max_functions = 30) {
|
|
| 160 |
# Priority order for CRAN functions |
|
| 161 | ! |
priority_order <- c( |
| 162 | ! |
"core_workflow", |
| 163 | ! |
"privacy_compliance", |
| 164 | ! |
"data_processing", |
| 165 | ! |
"analysis", |
| 166 | ! |
"visualization", |
| 167 | ! |
"utility" |
| 168 |
) |
|
| 169 | ||
| 170 | ! |
cran_functions <- character(0) |
| 171 | ||
| 172 | ! |
for (category in priority_order) {
|
| 173 | ! |
if (category %in% names(categories)) {
|
| 174 | ! |
category_functions <- categories[[category]] |
| 175 | ||
| 176 |
# Add functions from this category |
|
| 177 | ! |
remaining_slots <- max_functions - length(cran_functions) |
| 178 | ! |
if (remaining_slots > 0) {
|
| 179 | ! |
functions_to_add <- category_functions[1:min(remaining_slots, length(category_functions))] |
| 180 | ! |
cran_functions <- c(cran_functions, functions_to_add) |
| 181 |
} |
|
| 182 |
} |
|
| 183 |
} |
|
| 184 | ||
| 185 | ! |
return(cran_functions) |
| 186 |
} |
|
| 187 | ||
| 188 |
#' Get functions marked for deprecation |
|
| 189 |
#' |
|
| 190 |
#' @param categories Function categories |
|
| 191 |
#' @param cran_functions Functions selected for CRAN |
|
| 192 |
#' @return Functions marked for deprecation |
|
| 193 |
get_deprecated_functions <- function(categories, cran_functions) {
|
|
| 194 |
all_functions <- unlist(categories) |
|
| 195 |
deprecated_functions <- setdiff(all_functions, cran_functions) |
|
| 196 | ||
| 197 |
return(deprecated_functions) |
|
| 198 |
} |
|
| 199 | ||
| 200 |
#' Analyze function dependencies within categories |
|
| 201 |
#' |
|
| 202 |
#' @param function_analysis Function analysis results |
|
| 203 |
#' @param categories Function categories |
|
| 204 |
#' @return Dependency analysis |
|
| 205 |
analyze_category_dependencies <- function(function_analysis, categories) {
|
|
| 206 | ! |
dependency_analysis <- list() |
| 207 | ||
| 208 | ! |
for (category in names(categories)) {
|
| 209 | ! |
category_functions <- categories[[category]] |
| 210 | ! |
category_deps <- list() |
| 211 | ||
| 212 | ! |
for (func_name in category_functions) {
|
| 213 | ! |
if (func_name %in% names(function_analysis)) {
|
| 214 | ! |
func_deps <- function_analysis[[func_name]]$dependencies |
| 215 | ! |
category_deps[[func_name]] <- func_deps |
| 216 |
} |
|
| 217 |
} |
|
| 218 | ||
| 219 | ! |
dependency_analysis[[category]] <- category_deps |
| 220 |
} |
|
| 221 | ||
| 222 | ! |
return(dependency_analysis) |
| 223 |
} |
|
| 224 | ||
| 225 |
#' Test categorization system |
|
| 226 |
#' |
|
| 227 |
#' @return Test results |
|
| 228 |
test_categorization_system <- function() {
|
|
| 229 | ! |
cat("š§Ŗ Testing categorization system...\n")
|
| 230 | ||
| 231 |
# Test with sample function analysis |
|
| 232 | ! |
sample_analysis <- list( |
| 233 | ! |
analyze_transcripts = list(name = "analyze_transcripts", signature = "transcripts"), |
| 234 | ! |
privacy_audit = list(name = "privacy_audit", signature = "data"), |
| 235 | ! |
load_zoom_transcript = list(name = "load_zoom_transcript", signature = "file"), |
| 236 | ! |
plot_users = list(name = "plot_users", signature = "data"), |
| 237 | ! |
make_roster_small = list(name = "make_roster_small", signature = "roster") |
| 238 |
) |
|
| 239 | ||
| 240 | ! |
categories <- categorize_functions(sample_analysis) |
| 241 | ||
| 242 | ! |
cat("ā
Categorization test completed\n")
|
| 243 | ! |
print_category_summary(categories) |
| 244 | ||
| 245 | ! |
return(categories) |
| 246 |
} |
| 1 |
#' Function Deprecation System |
|
| 2 |
#' |
|
| 3 |
#' @description Implements function deprecation with warnings and migration guides |
|
| 4 |
#' @keywords internal |
|
| 5 |
#' @noRd |
|
| 6 | ||
| 7 |
#' Add deprecation warning to function |
|
| 8 |
#' |
|
| 9 |
#' @param function_name Name of function to deprecate |
|
| 10 |
#' @param replacement_function Suggested replacement function |
|
| 11 |
#' @param removal_version Version when function will be removed |
|
| 12 |
#' @return Deprecation warning message |
|
| 13 |
add_deprecation_warning <- function(function_name, replacement_function = NULL, removal_version = "2.0.0") {
|
|
| 14 |
# Create deprecation warning message |
|
| 15 | ! |
warning_msg <- paste0( |
| 16 | ! |
"Function '", function_name, "' is deprecated and will be removed in version ", |
| 17 | ! |
removal_version, "." |
| 18 |
) |
|
| 19 | ||
| 20 | ! |
if (!is.null(replacement_function)) {
|
| 21 | ! |
warning_msg <- paste0( |
| 22 | ! |
warning_msg, |
| 23 | ! |
" Use '", replacement_function, "' instead." |
| 24 |
) |
|
| 25 |
} |
|
| 26 | ||
| 27 | ! |
warning_msg <- paste0( |
| 28 | ! |
warning_msg, |
| 29 | ! |
" See ?", function_name, " for migration guidance." |
| 30 |
) |
|
| 31 | ||
| 32 | ! |
return(warning_msg) |
| 33 |
} |
|
| 34 | ||
| 35 |
#' Generate migration guide for deprecated functions |
|
| 36 |
#' |
|
| 37 |
#' @param deprecated_functions Functions marked for deprecation |
|
| 38 |
#' @param migration_recommendations Migration recommendations |
|
| 39 |
#' @return Migration guide content |
|
| 40 |
generate_migration_guide <- function(deprecated_functions, migration_recommendations) {
|
|
| 41 | ! |
cat("š Generating migration guide for deprecated functions...\n")
|
| 42 | ||
| 43 | ! |
migration_guide <- "# Function Migration Guide\n\n" |
| 44 | ! |
migration_guide <- paste0( |
| 45 | ! |
migration_guide, |
| 46 | ! |
"This guide helps you migrate from deprecated functions to their replacements.\n\n" |
| 47 |
) |
|
| 48 | ||
| 49 | ! |
migration_guide <- paste0( |
| 50 | ! |
migration_guide, |
| 51 | ! |
"## Deprecation Timeline\n\n" |
| 52 |
) |
|
| 53 | ! |
migration_guide <- paste0( |
| 54 | ! |
migration_guide, |
| 55 | ! |
"- **Current Version**: Functions are deprecated with warnings\n" |
| 56 |
) |
|
| 57 | ! |
migration_guide <- paste0( |
| 58 | ! |
migration_guide, |
| 59 | ! |
"- **Version 2.0.0**: Deprecated functions will be removed\n\n" |
| 60 |
) |
|
| 61 | ||
| 62 | ! |
migration_guide <- paste0( |
| 63 | ! |
migration_guide, |
| 64 | ! |
"## Migration Instructions\n\n" |
| 65 |
) |
|
| 66 | ||
| 67 | ! |
for (func_name in deprecated_functions) {
|
| 68 | ! |
if (func_name %in% names(migration_recommendations)) {
|
| 69 | ! |
rec <- migration_recommendations[[func_name]] |
| 70 | ||
| 71 | ! |
migration_guide <- paste0( |
| 72 | ! |
migration_guide, |
| 73 | ! |
"### ", func_name, "\n\n" |
| 74 |
) |
|
| 75 | ||
| 76 | ! |
migration_guide <- paste0( |
| 77 | ! |
migration_guide, |
| 78 | ! |
"- **Status**: Deprecated (will be removed in v2.0.0)\n" |
| 79 |
) |
|
| 80 | ! |
migration_guide <- paste0( |
| 81 | ! |
migration_guide, |
| 82 | ! |
"- **Replacement**: ", rec$replacement_function, "\n" |
| 83 |
) |
|
| 84 | ! |
migration_guide <- paste0( |
| 85 | ! |
migration_guide, |
| 86 | ! |
"- **Impact Level**: ", rec$impact_level, "\n" |
| 87 |
) |
|
| 88 | ! |
migration_guide <- paste0( |
| 89 | ! |
migration_guide, |
| 90 | ! |
"- **Migration**: ", rec$migration_strategy, "\n\n" |
| 91 |
) |
|
| 92 |
} |
|
| 93 |
} |
|
| 94 | ||
| 95 | ! |
migration_guide <- paste0( |
| 96 | ! |
migration_guide, |
| 97 | ! |
"## Getting Help\n\n" |
| 98 |
) |
|
| 99 | ! |
migration_guide <- paste0( |
| 100 | ! |
migration_guide, |
| 101 | ! |
"If you need help with migration, please:\n" |
| 102 |
) |
|
| 103 | ! |
migration_guide <- paste0( |
| 104 | ! |
migration_guide, |
| 105 | ! |
"1. Check the function documentation: `?", func_name, "`\n" |
| 106 |
) |
|
| 107 | ! |
migration_guide <- paste0( |
| 108 | ! |
migration_guide, |
| 109 | ! |
"2. Review the package vignettes\n" |
| 110 |
) |
|
| 111 | ! |
migration_guide <- paste0( |
| 112 | ! |
migration_guide, |
| 113 | ! |
"3. Open an issue on GitHub if you need assistance\n\n" |
| 114 |
) |
|
| 115 | ||
| 116 | ! |
cat("ā
Migration guide generated\n")
|
| 117 | ||
| 118 | ! |
return(migration_guide) |
| 119 |
} |
|
| 120 | ||
| 121 |
#' Create deprecation warnings for functions |
|
| 122 |
#' |
|
| 123 |
#' @param deprecated_functions Functions to deprecate |
|
| 124 |
#' @param migration_recommendations Migration recommendations |
|
| 125 |
#' @return Deprecation warnings |
|
| 126 |
create_deprecation_warnings <- function(deprecated_functions, migration_recommendations) {
|
|
| 127 | ! |
cat("ā ļø Creating deprecation warnings...\n")
|
| 128 | ||
| 129 | ! |
deprecation_warnings <- list() |
| 130 | ||
| 131 | ! |
for (func_name in deprecated_functions) {
|
| 132 | ! |
if (func_name %in% names(migration_recommendations)) {
|
| 133 | ! |
rec <- migration_recommendations[[func_name]] |
| 134 | ||
| 135 | ! |
warning_msg <- add_deprecation_warning( |
| 136 | ! |
func_name, |
| 137 | ! |
rec$replacement_function, |
| 138 | ! |
"2.0.0" |
| 139 |
) |
|
| 140 | ||
| 141 | ! |
deprecation_warnings[[func_name]] <- warning_msg |
| 142 |
} |
|
| 143 |
} |
|
| 144 | ||
| 145 | ! |
cat("ā
Deprecation warnings created for", length(deprecation_warnings), "functions\n")
|
| 146 | ||
| 147 | ! |
return(deprecation_warnings) |
| 148 |
} |
|
| 149 | ||
| 150 |
#' Update NAMESPACE for deprecation |
|
| 151 |
#' |
|
| 152 |
#' @param cran_functions Functions to keep in NAMESPACE |
|
| 153 |
#' @param deprecated_functions Functions to remove from NAMESPACE |
|
| 154 |
#' @return Updated NAMESPACE content |
|
| 155 |
update_namespace_for_deprecation <- function(cran_functions, deprecated_functions) {
|
|
| 156 | ! |
cat("š Updating NAMESPACE for deprecation...\n")
|
| 157 | ||
| 158 |
# Read current NAMESPACE |
|
| 159 | ! |
namespace_lines <- readLines("NAMESPACE")
|
| 160 | ||
| 161 |
# Keep only CRAN functions in exports |
|
| 162 | ! |
new_namespace_lines <- character(0) |
| 163 | ||
| 164 | ! |
for (line in namespace_lines) {
|
| 165 | ! |
if (grepl("^export\\(", line)) {
|
| 166 |
# Extract function name |
|
| 167 | ! |
func_name <- gsub("^export\\(([^)]+)\\)", "\\1", line)
|
| 168 | ! |
func_name <- gsub('"', "", func_name)
|
| 169 | ||
| 170 |
# Keep function if it's in CRAN functions |
|
| 171 | ! |
if (func_name %in% cran_functions || func_name == "%>%") {
|
| 172 | ! |
new_namespace_lines <- c(new_namespace_lines, line) |
| 173 |
} |
|
| 174 |
} else {
|
|
| 175 |
# Keep non-export lines |
|
| 176 | ! |
new_namespace_lines <- c(new_namespace_lines, line) |
| 177 |
} |
|
| 178 |
} |
|
| 179 | ||
| 180 | ! |
cat("ā
NAMESPACE updated - removed", length(deprecated_functions), "deprecated functions\n")
|
| 181 | ||
| 182 | ! |
return(new_namespace_lines) |
| 183 |
} |
|
| 184 | ||
| 185 |
#' Create deprecation documentation |
|
| 186 |
#' |
|
| 187 |
#' @param deprecated_functions Functions marked for deprecation |
|
| 188 |
#' @param migration_recommendations Migration recommendations |
|
| 189 |
#' @return Deprecation documentation |
|
| 190 |
create_deprecation_documentation <- function(deprecated_functions, migration_recommendations) {
|
|
| 191 | ! |
cat("š Creating deprecation documentation...\n")
|
| 192 | ||
| 193 |
# Create deprecation vignette content |
|
| 194 | ! |
vignette_content <- generate_migration_guide(deprecated_functions, migration_recommendations) |
| 195 | ||
| 196 |
# Create individual function documentation updates |
|
| 197 | ! |
function_docs <- list() |
| 198 | ||
| 199 | ! |
for (func_name in deprecated_functions) {
|
| 200 | ! |
if (func_name %in% names(migration_recommendations)) {
|
| 201 | ! |
rec <- migration_recommendations[[func_name]] |
| 202 | ||
| 203 | ! |
doc_content <- paste0( |
| 204 | ! |
"#' @title ", func_name, " (Deprecated)\n", |
| 205 | ! |
"#' @description This function is deprecated and will be removed in version 2.0.0.\n", |
| 206 | ! |
"#' @details ", rec$migration_strategy, "\n", |
| 207 | ! |
"#' @param ... Function parameters (see replacement function)\n", |
| 208 | ! |
"#' @return Function output (see replacement function)\n", |
| 209 | ! |
"#' @seealso ", rec$replacement_function, "\n", |
| 210 | ! |
"#' @keywords internal\n", |
| 211 | ! |
"#' @export\n" |
| 212 |
) |
|
| 213 | ||
| 214 | ! |
function_docs[[func_name]] <- doc_content |
| 215 |
} |
|
| 216 |
} |
|
| 217 | ||
| 218 | ! |
cat("ā
Deprecation documentation created\n")
|
| 219 | ||
| 220 | ! |
return(list( |
| 221 | ! |
vignette_content = vignette_content, |
| 222 | ! |
function_docs = function_docs |
| 223 |
)) |
|
| 224 |
} |
|
| 225 | ||
| 226 |
#' Validate deprecation implementation |
|
| 227 |
#' |
|
| 228 |
#' @param cran_functions Functions selected for CRAN |
|
| 229 |
#' @param deprecated_functions Functions marked for deprecation |
|
| 230 |
#' @param migration_recommendations Migration recommendations |
|
| 231 |
#' @return Validation results |
|
| 232 |
validate_deprecation_implementation <- function(cran_functions, deprecated_functions, migration_recommendations) {
|
|
| 233 | ! |
cat("ā
Validating deprecation implementation...\n")
|
| 234 | ||
| 235 | ! |
validation_results <- list( |
| 236 | ! |
no_overlap = length(intersect(cran_functions, deprecated_functions)) == 0, |
| 237 | ! |
all_deprecated_have_migration = all(deprecated_functions %in% names(migration_recommendations)), |
| 238 | ! |
cran_functions_count = length(cran_functions), |
| 239 | ! |
deprecated_functions_count = length(deprecated_functions), |
| 240 | ! |
total_functions_consistent = length(cran_functions) + length(deprecated_functions) > 0 |
| 241 |
) |
|
| 242 | ||
| 243 |
# Print validation results |
|
| 244 | ! |
cat("š Deprecation Validation Results:\n")
|
| 245 | ! |
for (check in names(validation_results)) {
|
| 246 | ! |
status <- if (validation_results[[check]]) "ā " else "ā" |
| 247 | ! |
cat(sprintf(" %-30s: %s\n", check, status))
|
| 248 |
} |
|
| 249 | ! |
cat("\n")
|
| 250 | ||
| 251 | ! |
return(validation_results) |
| 252 |
} |
|
| 253 | ||
| 254 |
#' Generate deprecation summary report |
|
| 255 |
#' |
|
| 256 |
#' @param cran_functions Functions selected for CRAN |
|
| 257 |
#' @param deprecated_functions Functions marked for deprecation |
|
| 258 |
#' @param migration_recommendations Migration recommendations |
|
| 259 |
#' @param validation_results Validation results |
|
| 260 |
#' @return Deprecation summary report |
|
| 261 |
generate_deprecation_summary_report <- function(cran_functions, deprecated_functions, migration_recommendations, validation_results) {
|
|
| 262 | ! |
cat("š Generating deprecation summary report...\n")
|
| 263 | ||
| 264 | ! |
report <- list( |
| 265 | ! |
summary = list( |
| 266 | ! |
cran_functions = length(cran_functions), |
| 267 | ! |
deprecated_functions = length(deprecated_functions), |
| 268 | ! |
total_functions = length(cran_functions) + length(deprecated_functions), |
| 269 | ! |
migration_guides_created = length(migration_recommendations) |
| 270 |
), |
|
| 271 | ! |
cran_functions = cran_functions, |
| 272 | ! |
deprecated_functions = deprecated_functions, |
| 273 | ! |
migration_recommendations = migration_recommendations, |
| 274 | ! |
validation_results = validation_results, |
| 275 | ! |
generated_at = Sys.time() |
| 276 |
) |
|
| 277 | ||
| 278 | ! |
cat("ā
Deprecation summary report generated\n")
|
| 279 | ||
| 280 | ! |
return(report) |
| 281 |
} |
|
| 282 | ||
| 283 |
#' Test deprecation system |
|
| 284 |
#' |
|
| 285 |
#' @return Test results |
|
| 286 |
test_deprecation_system <- function() {
|
|
| 287 | ! |
cat("š§Ŗ Testing deprecation system...\n")
|
| 288 | ||
| 289 |
# Test with sample data |
|
| 290 | ! |
sample_cran_functions <- c("analyze_transcripts", "load_zoom_transcript", "privacy_audit")
|
| 291 | ! |
sample_deprecated_functions <- c("old_function1", "old_function2")
|
| 292 | ||
| 293 | ! |
sample_migration_recommendations <- list( |
| 294 | ! |
old_function1 = list( |
| 295 | ! |
replacement_function = "new_function1", |
| 296 | ! |
migration_strategy = "Replace old_function1 with new_function1", |
| 297 | ! |
impact_level = "medium_impact" |
| 298 |
), |
|
| 299 | ! |
old_function2 = list( |
| 300 | ! |
replacement_function = "new_function2", |
| 301 | ! |
migration_strategy = "Replace old_function2 with new_function2", |
| 302 | ! |
impact_level = "low_impact" |
| 303 |
) |
|
| 304 |
) |
|
| 305 | ||
| 306 | ! |
migration_guide <- generate_migration_guide(sample_deprecated_functions, sample_migration_recommendations) |
| 307 | ! |
deprecation_warnings <- create_deprecation_warnings(sample_deprecated_functions, sample_migration_recommendations) |
| 308 | ! |
validation_results <- validate_deprecation_implementation(sample_cran_functions, sample_deprecated_functions, sample_migration_recommendations) |
| 309 | ||
| 310 | ! |
cat("ā
Deprecation system test completed\n")
|
| 311 | ||
| 312 | ! |
return(list( |
| 313 | ! |
migration_guide = migration_guide, |
| 314 | ! |
deprecation_warnings = deprecation_warnings, |
| 315 | ! |
validation_results = validation_results |
| 316 |
)) |
|
| 317 |
} |
| 1 |
#' Privacy Audit |
|
| 2 |
#' |
|
| 3 |
#' Summarize which identifier columns were present and how many values were masked. |
|
| 4 |
#' |
|
| 5 |
#' @param data A tibble to audit. |
|
| 6 |
#' @param id_columns Columns treated as identifiers (same default as ensure_privacy). |
|
| 7 |
#' @return A tibble with columns: column, values, non_empty, masked_estimate. |
|
| 8 |
#' @export |
|
| 9 |
privacy_audit <- function( |
|
| 10 |
data = NULL, |
|
| 11 |
id_columns = c("preferred_name", "name", "first_last", "name_raw", "student_id", "email")) {
|
|
| 12 | 1x |
if (!tibble::is_tibble(data)) stop("`data` must be a tibble")
|
| 13 | 2x |
present <- intersect(id_columns, names(data)) |
| 14 | 2x |
if (length(present) == 0) {
|
| 15 | 1x |
return(tibble::tibble( |
| 16 | 1x |
column = character(0), |
| 17 | 1x |
values = integer(0), |
| 18 | 1x |
non_empty = integer(0), |
| 19 | 1x |
masked_estimate = integer(0) |
| 20 |
)) |
|
| 21 |
} |
|
| 22 | 1x |
rows <- lapply(present, function(col) {
|
| 23 | 2x |
v <- data[[col]] |
| 24 | 2x |
total <- length(v) |
| 25 | 2x |
non_empty <- sum(!is.na(v) & nzchar(as.character(v))) |
| 26 | 2x |
masked <- sum(grepl("^Student\\s+\\d+$", as.character(v)))
|
| 27 | 2x |
tibble::tibble(column = col, values = total, non_empty = non_empty, masked_estimate = masked) |
| 28 |
}) |
|
| 29 | 1x |
dplyr::bind_rows(rows) |
| 30 |
} |
| 1 |
#' Create Course Information Tibble |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble containing course information that can be used |
|
| 4 |
#' with `create_session_mapping()` to map Zoom recordings to specific courses. |
|
| 5 |
#' |
|
| 6 |
#' @param dept Department codes (e.g., "CS", "MATH", "LTF") |
|
| 7 |
#' @param course Course numbers (e.g., "101", "250", "201") |
|
| 8 |
#' @param section Section numbers (e.g., "1", "2", "3") |
|
| 9 |
#' @param instructor Instructor names |
|
| 10 |
#' @param session_length_hours Length of each session in hours |
|
| 11 |
#' @param semester_start_mdy Semester start date in "MMM DD, YYYY" format |
|
| 12 |
#' @param semester_end_mdy Semester end date in "MMM DD, YYYY" format |
|
| 13 |
#' @param session_days Days of the week when sessions occur (e.g., c("Mon", "Wed"))
|
|
| 14 |
#' @param session_times Times when sessions occur (e.g., c("10:00", "14:00"))
|
|
| 15 |
#' |
|
| 16 |
#' @return A tibble with course information containing: |
|
| 17 |
#' - dept: Department code |
|
| 18 |
#' - course: Course number |
|
| 19 |
#' - section: Section number |
|
| 20 |
#' - instructor: Instructor name |
|
| 21 |
#' - session_length_hours: Length of each session |
|
| 22 |
#' - semester_start: Semester start date |
|
| 23 |
#' - semester_end: Semester end date |
|
| 24 |
#' - session_days: Days of the week for sessions |
|
| 25 |
#' - session_times: Times for sessions |
|
| 26 |
#' |
|
| 27 |
#' @export |
|
| 28 |
#' |
|
| 29 |
#' @examples |
|
| 30 |
#' # Single course with multiple sections |
|
| 31 |
#' course_info <- create_course_info( |
|
| 32 |
#' dept = c("CS", "CS", "CS"),
|
|
| 33 |
#' course = c("101", "101", "101"),
|
|
| 34 |
#' section = c("1", "2", "3"),
|
|
| 35 |
#' instructor = c("Dr. Smith", "Dr. Smith", "Dr. Johnson"),
|
|
| 36 |
#' session_length_hours = c(1.5, 1.5, 1.5), |
|
| 37 |
#' session_days = c("Mon", "Mon", "Tue"),
|
|
| 38 |
#' session_times = c("10:00", "14:00", "10:00")
|
|
| 39 |
#' ) |
|
| 40 |
#' |
|
| 41 |
#' # Multiple courses |
|
| 42 |
#' course_info <- create_course_info( |
|
| 43 |
#' dept = c("CS", "MATH", "LTF"),
|
|
| 44 |
#' course = c("101", "250", "201"),
|
|
| 45 |
#' section = c("1", "1", "1"),
|
|
| 46 |
#' instructor = c("Dr. Smith", "Dr. Smith", "Dr. Smith"),
|
|
| 47 |
#' session_length_hours = c(1.5, 2.0, 1.5), |
|
| 48 |
#' session_days = c("Mon", "Tue", "Wed"),
|
|
| 49 |
#' session_times = c("10:00", "09:00", "14:00")
|
|
| 50 |
#' ) |
|
| 51 |
create_course_info <- function( |
|
| 52 |
dept = NULL, |
|
| 53 |
course = NULL, |
|
| 54 |
section = NULL, |
|
| 55 |
instructor = NULL, |
|
| 56 |
session_length_hours = 1.5, |
|
| 57 |
semester_start_mdy = "Jan 01, 2024", |
|
| 58 |
semester_end_mdy = "May 15, 2024", |
|
| 59 |
session_days = NULL, |
|
| 60 |
session_times = NULL) {
|
|
| 61 |
# DEPRECATED: This function will be removed in the next version |
|
| 62 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 63 | 39x |
warning("Function 'create_course_info' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 64 | ||
| 65 |
# Input validation |
|
| 66 | 39x |
if (length(unique(c( |
| 67 | 39x |
length(dept), length(course), length(section), |
| 68 | 39x |
length(instructor), length(session_length_hours) |
| 69 | 39x |
))) != 1) {
|
| 70 | 2x |
stop("All input vectors must have the same length")
|
| 71 |
} |
|
| 72 | ||
| 73 | 37x |
if (!is.null(session_days) && length(session_days) != length(dept)) {
|
| 74 | 2x |
stop("session_days must have the same length as other inputs")
|
| 75 |
} |
|
| 76 | ||
| 77 | 35x |
if (!is.null(session_times) && length(session_times) != length(dept)) {
|
| 78 | 1x |
stop("session_times must have the same length as other inputs")
|
| 79 |
} |
|
| 80 | ||
| 81 |
# Create the tibble |
|
| 82 | 34x |
result <- tibble::tibble( |
| 83 | 34x |
dept = as.character(dept), |
| 84 | 34x |
course = as.character(course), |
| 85 | 34x |
section = as.character(section), |
| 86 | 34x |
instructor = as.character(instructor), |
| 87 | 34x |
session_length_hours = as.numeric(session_length_hours), |
| 88 | 34x |
semester_start = lubridate::mdy(semester_start_mdy), |
| 89 | 34x |
semester_end = lubridate::mdy(semester_end_mdy) |
| 90 |
) |
|
| 91 | ||
| 92 |
# Add optional session information |
|
| 93 | 34x |
if (!is.null(session_days)) {
|
| 94 | 2x |
result$session_days <- as.character(session_days) |
| 95 |
} |
|
| 96 | ||
| 97 | 34x |
if (!is.null(session_times)) {
|
| 98 | 2x |
result$session_times <- as.character(session_times) |
| 99 |
} |
|
| 100 | ||
| 101 |
# Add course identifier - using base R to avoid potential dplyr issues |
|
| 102 | 34x |
result$course_id <- paste(result$dept, result$course, result$section, sep = "_") |
| 103 | 34x |
result$course_name <- paste(result$dept, result$course, "Section", result$section) |
| 104 | ||
| 105 |
# Sort by department, course number, and section - using base R |
|
| 106 | 34x |
result[order(result$dept, result$course, result$section), ] |
| 107 |
} |
| 1 |
#' Hash Names Consistently for Privacy-Aware Matching |
|
| 2 |
#' |
|
| 3 |
#' Creates consistent hashes for names to enable matching while maintaining privacy. |
|
| 4 |
#' This function normalizes names and generates deterministic hashes that can be |
|
| 5 |
#' used for cross-session matching without exposing real names. |
|
| 6 |
#' |
|
| 7 |
#' @param names Character vector of names to hash |
|
| 8 |
#' @param salt Character string to add to hashes for security (default: package name) |
|
| 9 |
#' @param normalize_names Logical, whether to normalize names before hashing (default: TRUE) |
|
| 10 |
#' |
|
| 11 |
#' @return Character vector of consistent hashes |
|
| 12 |
#' @export |
|
| 13 |
#' |
|
| 14 |
#' @examples |
|
| 15 |
#' # Basic usage |
|
| 16 |
#' hash_name_consistently(c("John Smith", "J. Smith", "Smith, John"))
|
|
| 17 |
#' |
|
| 18 |
#' # With normalization (handles variations) |
|
| 19 |
#' hash_name_consistently(c("Tom", "Thomas", "Tommy"), normalize_names = TRUE)
|
|
| 20 |
#' |
|
| 21 |
#' # Without normalization (exact matching only) |
|
| 22 |
#' hash_name_consistently(c("Tom", "Thomas", "Tommy"), normalize_names = FALSE)
|
|
| 23 |
hash_name_consistently <- function(names = NULL, |
|
| 24 |
salt = "zoomstudentengagement", |
|
| 25 |
normalize_names = TRUE) {
|
|
| 26 |
# Input validation |
|
| 27 | 20x |
if (!is.character(names)) {
|
| 28 | ! |
stop("names must be a character vector", call. = FALSE)
|
| 29 |
} |
|
| 30 | 20x |
if (!is.character(salt) || length(salt) != 1) {
|
| 31 | ! |
stop("salt must be a single character string", call. = FALSE)
|
| 32 |
} |
|
| 33 | 20x |
if (!is.logical(normalize_names) || length(normalize_names) != 1) {
|
| 34 | ! |
stop("normalize_names must be a single logical value", call. = FALSE)
|
| 35 |
} |
|
| 36 | ||
| 37 |
# Handle empty input |
|
| 38 | 20x |
if (length(names) == 0) {
|
| 39 | 2x |
return(character(0)) |
| 40 |
} |
|
| 41 | ||
| 42 |
# Normalize names if requested |
|
| 43 | 18x |
if (normalize_names) {
|
| 44 | 18x |
normalized_names <- normalize_name_for_matching(names) |
| 45 |
} else {
|
|
| 46 | ! |
normalized_names <- names |
| 47 |
} |
|
| 48 | ||
| 49 |
# Generate consistent hashes |
|
| 50 |
# Use digest package for secure hashing |
|
| 51 | 18x |
hashes <- sapply(normalized_names, function(name) {
|
| 52 | 38x |
if (is.na(name) || nchar(trimws(name)) == 0) {
|
| 53 | ! |
return(NA_character_) |
| 54 |
} |
|
| 55 | ||
| 56 |
# Create hash input: normalized name + salt |
|
| 57 | 38x |
hash_input <- paste0(trimws(name), "|", salt) |
| 58 | ||
| 59 |
# Generate hash using digest package |
|
| 60 | 38x |
hash <- digest::digest(hash_input, algo = "sha256", serialize = FALSE) |
| 61 | ||
| 62 |
# Return a shorter, more readable hash (first 8 characters) |
|
| 63 | 38x |
substr(hash, 1, 8) |
| 64 |
}) |
|
| 65 | ||
| 66 |
# Ensure we return a character vector |
|
| 67 | 18x |
as.character(hashes) |
| 68 |
} |
|
| 69 | ||
| 70 |
#' Normalize Name for Matching |
|
| 71 |
#' |
|
| 72 |
#' Internal function to normalize names for consistent matching. |
|
| 73 |
#' Handles common variations like case, punctuation, and formatting. |
|
| 74 |
#' |
|
| 75 |
#' @param names Character vector of names to normalize |
|
| 76 |
#' |
|
| 77 |
#' @return Character vector of normalized names |
|
| 78 |
#' @keywords internal |
|
| 79 |
normalize_name_for_matching <- function(names) {
|
|
| 80 |
# DEPRECATED: This function will be removed in the next version |
|
| 81 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 82 | 136x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 83 | ! |
warning("Function 'normalize_name_for_matching' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 84 |
} |
|
| 85 | ||
| 86 |
# Handle NA and empty values |
|
| 87 | 136x |
names[is.na(names)] <- "" |
| 88 | 136x |
names[nchar(trimws(names)) == 0] <- "" |
| 89 | ||
| 90 |
# Convert to lowercase |
|
| 91 | 136x |
normalized <- tolower(names) |
| 92 | ||
| 93 |
# Remove common punctuation and extra whitespace |
|
| 94 | 136x |
normalized <- gsub("[[:punct:]]", " ", normalized)
|
| 95 | 136x |
normalized <- gsub("\\s+", " ", normalized)
|
| 96 | 136x |
normalized <- trimws(normalized) |
| 97 | ||
| 98 |
# Handle common name variations |
|
| 99 |
# Remove titles (Dr., Prof., etc.) |
|
| 100 | 136x |
normalized <- gsub("\\b(dr|prof|professor|mr|mrs|ms|miss)\\b", "", normalized)
|
| 101 | 136x |
normalized <- trimws(normalized) |
| 102 | ||
| 103 |
# Sort name parts for consistent ordering |
|
| 104 |
# This handles "John Smith" vs "Smith, John" |
|
| 105 | 136x |
normalized <- sapply(strsplit(normalized, " "), function(parts) {
|
| 106 | 385x |
if (length(parts) == 0) {
|
| 107 | ! |
return("")
|
| 108 |
} |
|
| 109 | 385x |
paste(sort(parts), collapse = " ") |
| 110 |
}) |
|
| 111 | ||
| 112 |
# Return empty string for completely empty names |
|
| 113 | 136x |
normalized[nchar(normalized) == 0] <- "" |
| 114 | ||
| 115 | 136x |
normalized |
| 116 |
} |
| 1 |
#' UX System Integration |
|
| 2 |
#' |
|
| 3 |
#' @description Integrates function audit results with UX system |
|
| 4 |
#' @keywords internal |
|
| 5 |
#' @noRd |
|
| 6 | ||
| 7 |
#' Update UX categories based on function audit |
|
| 8 |
#' |
|
| 9 |
#' @param function_categories Function categories from audit |
|
| 10 |
#' @param cran_functions Functions selected for CRAN |
|
| 11 |
#' @return Updated UX categories |
|
| 12 |
update_ux_categories <- function(function_categories, cran_functions) {
|
|
| 13 | ! |
cat("š Updating UX categories based on function audit...\n")
|
| 14 | ||
| 15 |
# Define UX levels based on function importance and complexity |
|
| 16 | ! |
ux_categories <- list( |
| 17 | ! |
essential = character(0), # 5 most critical functions |
| 18 | ! |
common = character(0), # 10 commonly used functions |
| 19 | ! |
advanced = character(0), # 15 advanced functions |
| 20 | ! |
expert = character(0) # Remaining functions |
| 21 |
) |
|
| 22 | ||
| 23 |
# Prioritize functions for UX levels |
|
| 24 | ! |
prioritized_functions <- prioritize_functions_for_ux(function_categories, cran_functions) |
| 25 | ||
| 26 |
# Allocate functions to UX levels |
|
| 27 | ! |
ux_categories$essential <- prioritized_functions[1:min(5, length(prioritized_functions))] |
| 28 | ! |
ux_categories$common <- prioritized_functions[6:min(15, length(prioritized_functions))] |
| 29 | ! |
ux_categories$advanced <- prioritized_functions[16:min(30, length(prioritized_functions))] |
| 30 | ! |
ux_categories$expert <- prioritized_functions[31:length(prioritized_functions)] |
| 31 | ||
| 32 |
# Remove empty categories |
|
| 33 | ! |
ux_categories <- lapply(ux_categories, function(x) x[!is.na(x) & x != ""]) |
| 34 | ||
| 35 | ! |
cat("ā
UX categories updated successfully\n")
|
| 36 | ! |
print_ux_category_summary(ux_categories) |
| 37 | ||
| 38 | ! |
return(ux_categories) |
| 39 |
} |
|
| 40 | ||
| 41 |
#' Prioritize functions for UX levels |
|
| 42 |
#' |
|
| 43 |
#' @param function_categories Function categories from audit |
|
| 44 |
#' @param cran_functions Functions selected for CRAN |
|
| 45 |
#' @return Prioritized function list |
|
| 46 |
prioritize_functions_for_ux <- function(function_categories, cran_functions) {
|
|
| 47 |
# Priority order for UX (based on user workflow importance) |
|
| 48 | ! |
priority_order <- c( |
| 49 | ! |
"core_workflow", # Most important for users |
| 50 | ! |
"privacy_compliance", # Critical for compliance |
| 51 | ! |
"data_processing", # Core functionality |
| 52 | ! |
"analysis", # Analysis capabilities |
| 53 | ! |
"visualization", # Output functions |
| 54 | ! |
"utility" # Helper functions |
| 55 |
) |
|
| 56 | ||
| 57 | ! |
prioritized <- character(0) |
| 58 | ||
| 59 |
# Add functions in priority order |
|
| 60 | ! |
for (category in priority_order) {
|
| 61 | ! |
if (category %in% names(function_categories)) {
|
| 62 | ! |
category_functions <- function_categories[[category]] |
| 63 |
# Only include functions that are in CRAN selection |
|
| 64 | ! |
cran_category_functions <- intersect(category_functions, cran_functions) |
| 65 | ! |
prioritized <- c(prioritized, cran_category_functions) |
| 66 |
} |
|
| 67 |
} |
|
| 68 | ||
| 69 |
# Add any remaining CRAN functions |
|
| 70 | ! |
remaining_functions <- setdiff(cran_functions, prioritized) |
| 71 | ! |
prioritized <- c(prioritized, remaining_functions) |
| 72 | ||
| 73 | ! |
return(prioritized) |
| 74 |
} |
|
| 75 | ||
| 76 |
#' Print UX category summary |
|
| 77 |
#' |
|
| 78 |
#' @param ux_categories UX categories |
|
| 79 |
print_ux_category_summary <- function(ux_categories) {
|
|
| 80 | ! |
cat("\nš± UX CATEGORY SUMMARY\n")
|
| 81 | ! |
cat(paste(rep("=", 25), collapse = ""), "\n")
|
| 82 | ||
| 83 | ! |
for (level in names(ux_categories)) {
|
| 84 | ! |
count <- length(ux_categories[[level]]) |
| 85 | ! |
cat(sprintf("%-10s: %2d functions\n", level, count))
|
| 86 |
} |
|
| 87 | ! |
cat("\n")
|
| 88 |
} |
|
| 89 | ||
| 90 |
#' Update help system with new categories |
|
| 91 |
#' |
|
| 92 |
#' @param function_categories Function categories from audit |
|
| 93 |
#' @param ux_categories UX categories |
|
| 94 |
#' @return Updated help system |
|
| 95 |
update_help_system <- function(function_categories, ux_categories) {
|
|
| 96 | ! |
cat("š Updating help system with new categories...\n")
|
| 97 | ||
| 98 | ! |
help_system <- list( |
| 99 | ! |
getting_started = list( |
| 100 | ! |
functions = ux_categories$essential, |
| 101 | ! |
description = "Essential functions for getting started with transcript analysis" |
| 102 |
), |
|
| 103 | ! |
common_workflows = list( |
| 104 | ! |
functions = ux_categories$common, |
| 105 | ! |
description = "Commonly used functions for typical analysis workflows" |
| 106 |
), |
|
| 107 | ! |
advanced_features = list( |
| 108 | ! |
functions = ux_categories$advanced, |
| 109 | ! |
description = "Advanced functions for specialized analysis needs" |
| 110 |
), |
|
| 111 | ! |
expert_tools = list( |
| 112 | ! |
functions = ux_categories$expert, |
| 113 | ! |
description = "Expert-level functions for complex analysis scenarios" |
| 114 |
) |
|
| 115 |
) |
|
| 116 | ||
| 117 | ! |
cat("ā
Help system updated successfully\n")
|
| 118 | ||
| 119 | ! |
return(help_system) |
| 120 |
} |
|
| 121 | ||
| 122 |
#' Generate progressive disclosure configuration |
|
| 123 |
#' |
|
| 124 |
#' @param ux_categories UX categories |
|
| 125 |
#' @return Progressive disclosure configuration |
|
| 126 |
generate_progressive_disclosure_config <- function(ux_categories) {
|
|
| 127 | ! |
cat("šļø Generating progressive disclosure configuration...\n")
|
| 128 | ||
| 129 | ! |
disclosure_config <- list( |
| 130 | ! |
levels = list( |
| 131 | ! |
beginner = list( |
| 132 | ! |
functions = ux_categories$essential, |
| 133 | ! |
description = "Start here - essential functions for basic analysis", |
| 134 | ! |
show_advanced = FALSE |
| 135 |
), |
|
| 136 | ! |
intermediate = list( |
| 137 | ! |
functions = c(ux_categories$essential, ux_categories$common), |
| 138 | ! |
description = "Common workflows - essential + commonly used functions", |
| 139 | ! |
show_advanced = FALSE |
| 140 |
), |
|
| 141 | ! |
advanced = list( |
| 142 | ! |
functions = c(ux_categories$essential, ux_categories$common, ux_categories$advanced), |
| 143 | ! |
description = "Advanced analysis - includes specialized functions", |
| 144 | ! |
show_advanced = TRUE |
| 145 |
), |
|
| 146 | ! |
expert = list( |
| 147 | ! |
functions = c( |
| 148 | ! |
ux_categories$essential, ux_categories$common, |
| 149 | ! |
ux_categories$advanced, ux_categories$expert |
| 150 |
), |
|
| 151 | ! |
description = "Expert mode - all functions available", |
| 152 | ! |
show_advanced = TRUE |
| 153 |
) |
|
| 154 |
), |
|
| 155 | ! |
default_level = "beginner", |
| 156 | ! |
auto_progression = TRUE |
| 157 |
) |
|
| 158 | ||
| 159 | ! |
cat("ā
Progressive disclosure configuration generated\n")
|
| 160 | ||
| 161 | ! |
return(disclosure_config) |
| 162 |
} |
|
| 163 | ||
| 164 |
#' Update function help descriptions |
|
| 165 |
#' |
|
| 166 |
#' @param function_categories Function categories from audit |
|
| 167 |
#' @param ux_categories UX categories |
|
| 168 |
#' @return Updated function descriptions |
|
| 169 |
update_function_descriptions <- function(function_categories, ux_categories) {
|
|
| 170 | ! |
cat("š Updating function help descriptions...\n")
|
| 171 | ||
| 172 | ! |
descriptions <- list() |
| 173 | ||
| 174 | ! |
for (level in names(ux_categories)) {
|
| 175 | ! |
for (func_name in ux_categories[[level]]) {
|
| 176 | ! |
descriptions[[func_name]] <- list( |
| 177 | ! |
level = level, |
| 178 | ! |
category = get_function_category_by_name(func_name, function_categories), |
| 179 | ! |
description = generate_function_description(func_name, level) |
| 180 |
) |
|
| 181 |
} |
|
| 182 |
} |
|
| 183 | ||
| 184 | ! |
cat("ā
Function descriptions updated\n")
|
| 185 | ||
| 186 | ! |
return(descriptions) |
| 187 |
} |
|
| 188 | ||
| 189 |
#' Get function category by name |
|
| 190 |
#' |
|
| 191 |
#' @param func_name Function name |
|
| 192 |
#' @param function_categories Function categories |
|
| 193 |
#' @return Category name |
|
| 194 |
get_function_category_by_name <- function(func_name, function_categories) {
|
|
| 195 | ! |
for (category in names(function_categories)) {
|
| 196 | ! |
if (func_name %in% function_categories[[category]]) {
|
| 197 | ! |
return(category) |
| 198 |
} |
|
| 199 |
} |
|
| 200 | ! |
return("unknown")
|
| 201 |
} |
|
| 202 | ||
| 203 |
#' Generate function description based on UX level |
|
| 204 |
#' |
|
| 205 |
#' @param func_name Function name |
|
| 206 |
#' @param ux_level UX level |
|
| 207 |
#' @return Function description |
|
| 208 |
generate_function_description <- function(func_name, ux_level) {
|
|
| 209 | ! |
base_descriptions <- list( |
| 210 | ! |
analyze_transcripts = "Main function for analyzing Zoom transcripts", |
| 211 | ! |
load_zoom_transcript = "Load and parse Zoom transcript files", |
| 212 | ! |
privacy_audit = "Audit transcript data for privacy compliance", |
| 213 | ! |
plot_users = "Create visualizations of user engagement", |
| 214 | ! |
write_metrics = "Export analysis results to files" |
| 215 |
) |
|
| 216 | ||
| 217 | ! |
if (func_name %in% names(base_descriptions)) {
|
| 218 | ! |
base_desc <- base_descriptions[[func_name]] |
| 219 |
} else {
|
|
| 220 | ! |
base_desc <- paste("Function:", func_name)
|
| 221 |
} |
|
| 222 | ||
| 223 | ! |
level_prefixes <- list( |
| 224 | ! |
essential = "ā Essential: ", |
| 225 | ! |
common = "š Common: ", |
| 226 | ! |
advanced = "š§ Advanced: ", |
| 227 | ! |
expert = "ā” Expert: " |
| 228 |
) |
|
| 229 | ||
| 230 | ! |
if (ux_level %in% names(level_prefixes)) {
|
| 231 | ! |
return(paste0(level_prefixes[[ux_level]], base_desc)) |
| 232 |
} else {
|
|
| 233 | ! |
return(base_desc) |
| 234 |
} |
|
| 235 |
} |
|
| 236 | ||
| 237 |
#' Create UX integration report |
|
| 238 |
#' |
|
| 239 |
#' @param function_categories Function categories from audit |
|
| 240 |
#' @param cran_functions Functions selected for CRAN |
|
| 241 |
#' @param ux_categories UX categories |
|
| 242 |
#' @param help_system Help system |
|
| 243 |
#' @return UX integration report |
|
| 244 |
create_ux_integration_report <- function(function_categories, cran_functions, ux_categories, help_system) {
|
|
| 245 | ! |
cat("š Creating UX integration report...\n")
|
| 246 | ||
| 247 | ! |
report <- list( |
| 248 | ! |
summary = list( |
| 249 | ! |
total_functions = length(cran_functions), |
| 250 | ! |
essential_functions = length(ux_categories$essential), |
| 251 | ! |
common_functions = length(ux_categories$common), |
| 252 | ! |
advanced_functions = length(ux_categories$advanced), |
| 253 | ! |
expert_functions = length(ux_categories$expert) |
| 254 |
), |
|
| 255 | ! |
ux_categories = ux_categories, |
| 256 | ! |
help_system = help_system, |
| 257 | ! |
progressive_disclosure = generate_progressive_disclosure_config(ux_categories), |
| 258 | ! |
generated_at = Sys.time() |
| 259 |
) |
|
| 260 | ||
| 261 | ! |
cat("ā
UX integration report created\n")
|
| 262 | ||
| 263 | ! |
return(report) |
| 264 |
} |
|
| 265 | ||
| 266 |
#' Test UX integration system |
|
| 267 |
#' |
|
| 268 |
#' @return Test results |
|
| 269 |
test_ux_integration <- function() {
|
|
| 270 | ! |
cat("š§Ŗ Testing UX integration system...\n")
|
| 271 | ||
| 272 |
# Test with sample data |
|
| 273 | ! |
sample_categories <- list( |
| 274 | ! |
core_workflow = c("analyze_transcripts", "load_zoom_transcript"),
|
| 275 | ! |
privacy_compliance = c("privacy_audit", "ensure_privacy"),
|
| 276 | ! |
data_processing = c("consolidate_transcript"),
|
| 277 | ! |
analysis = c("summarize_transcript_metrics"),
|
| 278 | ! |
visualization = c("plot_users", "write_metrics"),
|
| 279 | ! |
utility = c("get_essential_functions")
|
| 280 |
) |
|
| 281 | ||
| 282 | ! |
sample_cran_functions <- c( |
| 283 | ! |
"analyze_transcripts", "load_zoom_transcript", "privacy_audit", |
| 284 | ! |
"ensure_privacy", "consolidate_transcript", "summarize_transcript_metrics", |
| 285 | ! |
"plot_users", "write_metrics", "get_essential_functions" |
| 286 |
) |
|
| 287 | ||
| 288 | ! |
ux_categories <- update_ux_categories(sample_categories, sample_cran_functions) |
| 289 | ! |
help_system <- update_help_system(sample_categories, ux_categories) |
| 290 | ||
| 291 | ! |
cat("ā
UX integration test completed\n")
|
| 292 | ||
| 293 | ! |
return(list( |
| 294 | ! |
ux_categories = ux_categories, |
| 295 | ! |
help_system = help_system |
| 296 |
)) |
|
| 297 |
} |
| 1 |
#' Process Zoom Transcript |
|
| 2 |
#' |
|
| 3 |
#' Process a Zoom recording transcript with given parameters and return tibble containing the consolidated and annotated comments. |
|
| 4 | ||
| 5 |
#' |
|
| 6 |
#' Original code posted by Conor Healy: |
|
| 7 |
#' https://ucbischool.slack.com/archives/C02A36407K9/p1631855705002000 Addition |
|
| 8 |
#' of `wordcount`, `wordcount_perc`, and `wpm` by Brooks Ambrose: |
|
| 9 |
#' https://gist.github.com/brooksambrose/1a8a673eb3bf884c1868ad4d80f08246 |
|
| 10 | ||
| 11 | ||
| 12 | ||
| 13 | ||
| 14 |
#' @param transcript_file_path File path of a .transcript.vtt file of a Zoom recording |
|
| 15 |
#' transcript. |
|
| 16 |
#' @param consolidate_comments Set to `TRUE` to consolidate consecutive comments |
|
| 17 |
#' from the same speaker with gaps of less than `max_pause_sec`. `FALSE` |
|
| 18 |
#' returns the results from the raw transcript. Defaults to `TRUE` |
|
| 19 |
#' @param max_pause_sec Maximum pause between comments to be consolidated. If |
|
| 20 |
#' the raw comments from the Zoom recording transcript contain 2 consecutive |
|
| 21 |
#' comments from the same speaker, and the time between the end of the first |
|
| 22 |
#' comment and start of the second comment is less than `max_pause_sec` |
|
| 23 |
#' seconds, then the comments will be consolidated. If the time between the |
|
| 24 |
#' comments is larger, they will not be consolidated. Defaults to 1. |
|
| 25 |
#' @param add_dead_air Set to `TRUE` to adds rows for any time between |
|
| 26 |
#' transcribed comments, labeled with the `dead_air_name` provided (or the |
|
| 27 |
#' default value of 'dead_air'). The resulting tibble will have rows |
|
| 28 |
#' accounting for the time from the beginning of the first comment to the end |
|
| 29 |
#' of the last one. Defaults to `TRUE`. |
|
| 30 |
#' @param dead_air_name Character string to label the `name` column in any rows |
|
| 31 |
#' added for dead air. Defaults to 'dead_air'. |
|
| 32 |
#' @param na_name Character string to label the `name` column in any rows where |
|
| 33 |
#' the transcript `name` is `NA`. Defaults to 'unknown'. |
|
| 34 |
#' @param transcript_df Tibble containing the comments from a Zoom recording transcript (which is generally the result of calling `load_zoom_transcript()`. |
|
| 35 |
#' |
|
| 36 |
#' @return A tibble containing the comments from a Zoom recording transcript |
|
| 37 |
#' |
|
| 38 |
#' @export |
|
| 39 |
#' |
|
| 40 |
#' @examples |
|
| 41 |
#' # Load a sample transcript from the package's extdata directory |
|
| 42 |
#' transcript_file <- system.file("extdata/transcripts/GMT20240124-202901_Recording.transcript.vtt",
|
|
| 43 |
#' package = "zoomstudentengagement" |
|
| 44 |
#' ) |
|
| 45 |
#' process_zoom_transcript(transcript_file_path = transcript_file) |
|
| 46 |
#' |
|
| 47 |
process_zoom_transcript <- function(transcript_file_path = "", |
|
| 48 |
consolidate_comments = TRUE, |
|
| 49 |
max_pause_sec = 1, |
|
| 50 |
add_dead_air = TRUE, |
|
| 51 |
dead_air_name = "dead_air", |
|
| 52 |
na_name = "unknown", |
|
| 53 |
transcript_df = NULL) {
|
|
| 54 | 292x |
if (is.null(transcript_df)) {
|
| 55 | 287x |
if (nzchar(transcript_file_path) && file.exists(transcript_file_path)) {
|
| 56 | 286x |
transcript_df <- zoomstudentengagement::load_zoom_transcript(transcript_file_path) |
| 57 |
} else {
|
|
| 58 | 1x |
return(NULL) |
| 59 |
} |
|
| 60 |
} |
|
| 61 | ||
| 62 | 291x |
if (tibble::is_tibble(transcript_df)) {
|
| 63 |
# Ensure time columns are of type hms (replacing lubridate::period to avoid segfaults) |
|
| 64 |
# Use base R operations to avoid dplyr segfaults |
|
| 65 | 283x |
transcript_df$start <- hms::as_hms(transcript_df$start) |
| 66 | 283x |
transcript_df$end <- hms::as_hms(transcript_df$end) |
| 67 | 283x |
transcript_df$duration <- as.numeric(transcript_df$duration) |
| 68 | ||
| 69 |
# Add begin time and prior speaker info using base R to avoid segfaults |
|
| 70 |
# Sort by start time for lag operations |
|
| 71 | 283x |
transcript_df <- transcript_df[order(transcript_df$start), ] |
| 72 | ||
| 73 |
# Calculate lag values using base R |
|
| 74 | 283x |
transcript_df$begin <- c(hms::hms(0), transcript_df$end[-length(transcript_df$end)]) |
| 75 | 283x |
transcript_df$prior_dead_air <- as.numeric(transcript_df$start - transcript_df$begin) |
| 76 | 283x |
transcript_df$prior_speaker <- c(NA, transcript_df$name[-length(transcript_df$name)]) |
| 77 | ||
| 78 |
# Reorder columns using base R |
|
| 79 | 283x |
col_order <- c("transcript_file", "comment_num", "name", "comment", "start", "end", "duration", "prior_dead_air")
|
| 80 | 283x |
other_cols <- setdiff(names(transcript_df), col_order) |
| 81 | 283x |
transcript_df <- transcript_df[, c(col_order, other_cols)] |
| 82 | ||
| 83 | ||
| 84 | 283x |
if (consolidate_comments) {
|
| 85 | 255x |
transcript_df <- zoomstudentengagement::consolidate_transcript(transcript_df, |
| 86 | 255x |
max_pause_sec = max_pause_sec |
| 87 |
) |
|
| 88 |
} |
|
| 89 | ||
| 90 | 283x |
if (add_dead_air) {
|
| 91 | 255x |
transcript_df <- zoomstudentengagement::add_dead_air_rows(transcript_df, |
| 92 | 255x |
dead_air_name = dead_air_name |
| 93 |
) |
|
| 94 |
} |
|
| 95 | ||
| 96 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 97 |
# Sort by start time |
|
| 98 | 283x |
return_df <- transcript_df[order(transcript_df$start), ] |
| 99 | ||
| 100 |
# Add comment numbers using base R |
|
| 101 | 283x |
return_df$comment_num <- seq_len(nrow(return_df)) |
| 102 | ||
| 103 |
# Handle NA names using base R |
|
| 104 | 283x |
return_df$name <- ifelse(is.na(return_df$name), na_name, return_df$name) |
| 105 | ||
| 106 |
# Convert to tibble to maintain expected return type |
|
| 107 | 283x |
return(tibble::as_tibble(return_df)) |
| 108 |
} |
|
| 109 | 8x |
NULL |
| 110 |
} |
| 1 |
#' Process all ideal course transcripts in batch |
|
| 2 |
#' |
|
| 3 |
#' Processes all ideal course transcripts (sessions 1-3) in batch mode, providing |
|
| 4 |
#' comprehensive analysis across multiple scenarios. This function is designed |
|
| 5 |
#' for testing, validation, and demonstration purposes using the package's |
|
| 6 |
#' synthetic ideal course data. |
|
| 7 |
#' |
|
| 8 |
#' @param include_roster Logical. Whether to include roster data in processing. |
|
| 9 |
#' Default: TRUE |
|
| 10 |
#' @param privacy_level Character. Privacy level for processing. Options: |
|
| 11 |
#' "full" (FERPA strict), "masked" (default), "none" (no privacy protection) |
|
| 12 |
#' @param output_format Character. Output format. Options: "list" (default), |
|
| 13 |
#' "data.frame", "summary" |
|
| 14 |
#' @param consolidate_comments Logical. Whether to consolidate consecutive comments. |
|
| 15 |
#' Default: TRUE |
|
| 16 |
#' @param add_dead_air Logical. Whether to add dead air rows. Default: TRUE |
|
| 17 |
#' @param names_exclude Character vector. Names to exclude from analysis. |
|
| 18 |
#' Default: c("dead_air")
|
|
| 19 |
#' |
|
| 20 |
#' @return List or data.frame with batch processing results containing: |
|
| 21 |
#' - session_data: Processed transcript data for each session |
|
| 22 |
#' - summary_metrics: Engagement metrics for each session |
|
| 23 |
#' - participation_patterns: Cross-session participation analysis |
|
| 24 |
#' - validation_results: Basic validation checks |
|
| 25 |
#' - processing_info: Metadata about the batch processing |
|
| 26 |
#' |
|
| 27 |
#' @importFrom utils read.csv |
|
| 28 |
#' @examples |
|
| 29 |
#' \dontrun{
|
|
| 30 |
#' # Process all ideal course sessions with default settings |
|
| 31 |
#' batch_results <- process_ideal_course_batch() |
|
| 32 |
#' |
|
| 33 |
#' # Process with custom privacy settings |
|
| 34 |
#' batch_results <- process_ideal_course_batch( |
|
| 35 |
#' privacy_level = "full", |
|
| 36 |
#' output_format = "summary" |
|
| 37 |
#' ) |
|
| 38 |
#' |
|
| 39 |
#' # Process without roster data |
|
| 40 |
#' batch_results <- process_ideal_course_batch( |
|
| 41 |
#' include_roster = FALSE, |
|
| 42 |
#' output_format = "data.frame" |
|
| 43 |
#' ) |
|
| 44 |
#' } |
|
| 45 |
#' |
|
| 46 |
#' @export |
|
| 47 |
#' @keywords deprecated |
|
| 48 |
process_ideal_course_batch <- function(include_roster = TRUE, |
|
| 49 |
privacy_level = "masked", |
|
| 50 |
output_format = "list", |
|
| 51 |
consolidate_comments = TRUE, |
|
| 52 |
add_dead_air = TRUE, |
|
| 53 |
names_exclude = c("dead_air")) {
|
|
| 54 |
# DEPRECATED: This function will be removed in the next version |
|
| 55 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 56 | 40x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 57 | ! |
warning("Function 'process_ideal_course_batch' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 58 |
} |
|
| 59 | ||
| 60 |
# Validate inputs |
|
| 61 | 40x |
if (!privacy_level %in% c("full", "masked", "none")) {
|
| 62 | 2x |
stop("privacy_level must be one of: 'full', 'masked', 'none'")
|
| 63 |
} |
|
| 64 | ||
| 65 | 38x |
if (!output_format %in% c("list", "data.frame", "summary")) {
|
| 66 | 2x |
stop("output_format must be one of: 'list', 'data.frame', 'summary'")
|
| 67 |
} |
|
| 68 | ||
| 69 |
# Get ideal course transcript directory |
|
| 70 | 36x |
transcript_dir <- system.file("extdata", "test_transcripts", package = "zoomstudentengagement")
|
| 71 | 36x |
if (!dir.exists(transcript_dir)) {
|
| 72 | ! |
stop("Ideal course transcript directory not found")
|
| 73 |
} |
|
| 74 | ||
| 75 |
# Define ideal course session files |
|
| 76 | 36x |
session_files <- c( |
| 77 | 36x |
"ideal_course_session1.vtt", |
| 78 | 36x |
"ideal_course_session2.vtt", |
| 79 | 36x |
"ideal_course_session3.vtt" |
| 80 |
) |
|
| 81 | ||
| 82 |
# Initialize results storage |
|
| 83 | 36x |
session_data <- list() |
| 84 | 36x |
summary_metrics <- list() |
| 85 | 36x |
participation_patterns <- list() |
| 86 | 36x |
processing_errors <- list() |
| 87 | ||
| 88 |
# Load roster data if requested |
|
| 89 | 36x |
roster_data <- NULL |
| 90 | 36x |
if (include_roster) {
|
| 91 | 35x |
roster_path <- file.path(transcript_dir, "ideal_course_roster.csv") |
| 92 | 35x |
if (file.exists(roster_path)) {
|
| 93 | 35x |
roster_data <- utils::read.csv(roster_path, stringsAsFactors = FALSE) |
| 94 |
} else {
|
|
| 95 | ! |
warning("Roster file not found, proceeding without roster data")
|
| 96 |
} |
|
| 97 |
} |
|
| 98 | ||
| 99 |
# Process each session |
|
| 100 | 36x |
for (i in seq_along(session_files)) {
|
| 101 | 108x |
session_file <- session_files[i] |
| 102 | 108x |
session_name <- paste0("session", i)
|
| 103 | 108x |
session_path <- file.path(transcript_dir, session_file) |
| 104 | ||
| 105 | 108x |
if (!file.exists(session_path)) {
|
| 106 | ! |
processing_errors[[session_name]] <- paste("File not found:", session_file)
|
| 107 | ! |
next |
| 108 |
} |
|
| 109 | ||
| 110 | 108x |
tryCatch( |
| 111 |
{
|
|
| 112 |
# Load and process transcript |
|
| 113 | 108x |
raw_transcript <- load_zoom_transcript(session_path) |
| 114 | ||
| 115 | 108x |
if (is.null(raw_transcript) || nrow(raw_transcript) == 0) {
|
| 116 | ! |
processing_errors[[session_name]] <- "Empty or invalid transcript" |
| 117 | ! |
next |
| 118 |
} |
|
| 119 | ||
| 120 |
# Process transcript with specified options |
|
| 121 | 108x |
processed_transcript <- process_zoom_transcript( |
| 122 | 108x |
transcript_file_path = session_path, |
| 123 | 108x |
consolidate_comments = consolidate_comments, |
| 124 | 108x |
add_dead_air = add_dead_air, |
| 125 | 108x |
dead_air_name = "dead_air", |
| 126 | 108x |
na_name = "unknown" |
| 127 |
) |
|
| 128 | ||
| 129 |
# Calculate summary metrics |
|
| 130 | 108x |
session_metrics <- summarize_transcript_metrics( |
| 131 | 108x |
transcript_file_path = session_path, |
| 132 | 108x |
names_exclude = names_exclude, |
| 133 | 108x |
consolidate_comments = consolidate_comments, |
| 134 | 108x |
add_dead_air = add_dead_air |
| 135 |
) |
|
| 136 | ||
| 137 |
# Store results |
|
| 138 | 108x |
session_data[[session_name]] <- processed_transcript |
| 139 | 108x |
summary_metrics[[session_name]] <- session_metrics |
| 140 | ||
| 141 |
# Extract participation patterns |
|
| 142 | 108x |
participants <- unique(processed_transcript$name) |
| 143 | 108x |
participants <- participants[!participants %in% names_exclude] |
| 144 | 108x |
participation_patterns[[session_name]] <- participants |
| 145 |
}, |
|
| 146 | 108x |
error = function(e) {
|
| 147 | ! |
processing_errors[[session_name]] <- e$message |
| 148 |
} |
|
| 149 |
) |
|
| 150 |
} |
|
| 151 | ||
| 152 |
# Create processing info |
|
| 153 | 36x |
processing_info <- list( |
| 154 | 36x |
timestamp = Sys.time(), |
| 155 | 36x |
privacy_level = privacy_level, |
| 156 | 36x |
sessions_processed = length(session_data), |
| 157 | 36x |
sessions_failed = length(processing_errors), |
| 158 | 36x |
total_participants = length(unique(unlist(participation_patterns))), |
| 159 | 36x |
processing_options = list( |
| 160 | 36x |
consolidate_comments = consolidate_comments, |
| 161 | 36x |
add_dead_air = add_dead_air, |
| 162 | 36x |
names_exclude = names_exclude, |
| 163 | 36x |
include_roster = include_roster |
| 164 |
) |
|
| 165 |
) |
|
| 166 | ||
| 167 |
# Create validation results |
|
| 168 | 36x |
validation_results <- list( |
| 169 | 36x |
all_sessions_loaded = length(session_data) == 3, |
| 170 | 36x |
no_processing_errors = length(processing_errors) == 0, |
| 171 | 36x |
data_consistency = all(sapply(session_data, function(x) !is.null(x) && nrow(x) > 0)), |
| 172 | 36x |
privacy_compliant = privacy_level != "none" |
| 173 |
) |
|
| 174 | ||
| 175 |
# Prepare output based on format |
|
| 176 | 36x |
if (output_format == "list") {
|
| 177 | 34x |
result <- list( |
| 178 | 34x |
session_data = session_data, |
| 179 | 34x |
summary_metrics = summary_metrics, |
| 180 | 34x |
participation_patterns = participation_patterns, |
| 181 | 34x |
validation_results = validation_results, |
| 182 | 34x |
processing_info = processing_info, |
| 183 | 34x |
processing_errors = processing_errors |
| 184 |
) |
|
| 185 | 2x |
} else if (output_format == "data.frame") {
|
| 186 |
# Combine all summary metrics into a single data frame |
|
| 187 | 1x |
all_metrics <- do.call(rbind, lapply(names(summary_metrics), function(session) {
|
| 188 | 3x |
if (!is.null(summary_metrics[[session]]) && nrow(summary_metrics[[session]]) > 0) {
|
| 189 | 3x |
summary_metrics[[session]]$session <- session |
| 190 | 3x |
summary_metrics[[session]] |
| 191 |
} else {
|
|
| 192 | ! |
NULL |
| 193 |
} |
|
| 194 |
})) |
|
| 195 | ||
| 196 | 1x |
result <- all_metrics |
| 197 | 1x |
} else if (output_format == "summary") {
|
| 198 |
# Create a summary data frame |
|
| 199 | 1x |
summary_df <- data.frame( |
| 200 | 1x |
session = names(session_data), |
| 201 | 1x |
participants = sapply(participation_patterns, length), |
| 202 | 1x |
total_comments = sapply(session_data, function(x) nrow(x[x$name != "dead_air", ])), |
| 203 | 1x |
total_duration = sapply(session_data, function(x) sum(x$duration[x$name != "dead_air"], na.rm = TRUE)), |
| 204 | 1x |
total_words = sapply(session_data, function(x) sum(x$wordcount[x$name != "dead_air"], na.rm = TRUE)), |
| 205 | 1x |
stringsAsFactors = FALSE |
| 206 |
) |
|
| 207 | ||
| 208 | 1x |
result <- summary_df |
| 209 |
} |
|
| 210 | ||
| 211 |
# Add attributes for provenance |
|
| 212 | 36x |
attr(result, "batch_processing") <- TRUE |
| 213 | 36x |
attr(result, "privacy_level") <- privacy_level |
| 214 | 36x |
attr(result, "processing_timestamp") <- processing_info$timestamp |
| 215 | ||
| 216 | 36x |
return(result) |
| 217 |
} |
|
| 218 | ||
| 219 |
#' Compare engagement patterns across ideal course sessions |
|
| 220 |
#' |
|
| 221 |
#' Analyzes and compares engagement patterns across all ideal course sessions, |
|
| 222 |
#' providing insights into participation trends, attendance patterns, and |
|
| 223 |
#' engagement metrics. |
|
| 224 |
#' |
|
| 225 |
#' @param batch_results Results from process_ideal_course_batch() |
|
| 226 |
#' @param comparison_metrics Character vector. Metrics to compare. Options: |
|
| 227 |
#' "total_comments", "duration", "wordcount", "wpm", "participation_rate". |
|
| 228 |
#' Default: c("total_comments", "duration")
|
|
| 229 |
#' @param visualization Logical. Whether to include visualization data. |
|
| 230 |
#' Default: TRUE |
|
| 231 |
#' @param include_roster_comparison Logical. Whether to include roster-based |
|
| 232 |
#' comparison. Default: TRUE |
|
| 233 |
#' |
|
| 234 |
#' @return List containing comparison results and insights: |
|
| 235 |
#' - comparison_data: Data frame with comparison metrics |
|
| 236 |
#' - insights: Text insights about patterns |
|
| 237 |
#' - trends: Trend analysis across sessions |
|
| 238 |
#' - visualization_data: Data prepared for plotting (if visualization = TRUE) |
|
| 239 |
#' - roster_analysis: Roster-based attendance analysis (if include_roster_comparison = TRUE) |
|
| 240 |
#' |
|
| 241 |
#' @examples |
|
| 242 |
#' \dontrun{
|
|
| 243 |
#' # Process batch data first |
|
| 244 |
#' batch_results <- process_ideal_course_batch() |
|
| 245 |
#' |
|
| 246 |
#' # Compare sessions |
|
| 247 |
#' comparison <- compare_ideal_sessions(batch_results) |
|
| 248 |
#' |
|
| 249 |
#' # Compare specific metrics |
|
| 250 |
#' comparison <- compare_ideal_sessions( |
|
| 251 |
#' batch_results, |
|
| 252 |
#' comparison_metrics = c("total_comments", "duration", "wpm")
|
|
| 253 |
#' ) |
|
| 254 |
#' } |
|
| 255 |
#' |
|
| 256 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 257 |
#' @export |
|
| 258 |
#' @keywords deprecated |
|
| 259 |
compare_ideal_sessions <- function(batch_results = NULL, |
|
| 260 |
comparison_metrics = c("total_comments", "duration"),
|
|
| 261 |
visualization = TRUE, |
|
| 262 |
include_roster_comparison = TRUE) {
|
|
| 263 |
# DEPRECATED: This function will be removed in the next version |
|
| 264 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 265 | 10x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 266 | ! |
warning("Function 'compare_ideal_sessions' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 267 |
} |
|
| 268 | ||
| 269 |
# Validate inputs |
|
| 270 | 10x |
valid_metrics <- c("total_comments", "duration", "wordcount", "wpm", "participation_rate")
|
| 271 | 10x |
if (!all(comparison_metrics %in% valid_metrics)) {
|
| 272 | 1x |
stop("Invalid comparison_metrics. Valid options: ", paste(valid_metrics, collapse = ", "))
|
| 273 |
} |
|
| 274 | ||
| 275 |
# Extract summary metrics from batch results |
|
| 276 | 9x |
if ("summary_metrics" %in% names(batch_results)) {
|
| 277 | 8x |
summary_metrics <- batch_results$summary_metrics |
| 278 |
} else {
|
|
| 279 | 1x |
stop("batch_results must contain summary_metrics from process_ideal_course_batch()")
|
| 280 |
} |
|
| 281 | ||
| 282 |
# Create comparison data frame |
|
| 283 | 8x |
comparison_data <- data.frame() |
| 284 | ||
| 285 | 8x |
for (session_name in names(summary_metrics)) {
|
| 286 | 24x |
session_metrics <- summary_metrics[[session_name]] |
| 287 | ||
| 288 | 24x |
if (!is.null(session_metrics) && nrow(session_metrics) > 0) {
|
| 289 |
# Calculate session-level metrics |
|
| 290 | 24x |
session_summary <- data.frame( |
| 291 | 24x |
session = session_name, |
| 292 | 24x |
total_participants = nrow(session_metrics), |
| 293 | 24x |
total_comments = sum(session_metrics$n, na.rm = TRUE), |
| 294 | 24x |
total_duration = sum(session_metrics$duration, na.rm = TRUE), |
| 295 | 24x |
total_words = sum(session_metrics$wordcount, na.rm = TRUE), |
| 296 | 24x |
avg_wpm = mean(session_metrics$wpm, na.rm = TRUE), |
| 297 | 24x |
participation_rate = nrow(session_metrics) / max(nrow(session_metrics), 1), |
| 298 | 24x |
stringsAsFactors = FALSE |
| 299 |
) |
|
| 300 | ||
| 301 | 24x |
comparison_data <- rbind(comparison_data, session_summary) |
| 302 |
} |
|
| 303 |
} |
|
| 304 | ||
| 305 |
# Generate insights |
|
| 306 | 8x |
insights <- generate_comparison_insights(comparison_data, comparison_metrics) |
| 307 | ||
| 308 |
# Analyze trends |
|
| 309 | 8x |
trends <- analyze_session_trends(comparison_data, comparison_metrics) |
| 310 | ||
| 311 |
# Prepare visualization data |
|
| 312 | 8x |
visualization_data <- NULL |
| 313 | 8x |
if (visualization) {
|
| 314 | 7x |
visualization_data <- prepare_visualization_data(comparison_data, comparison_metrics) |
| 315 |
} |
|
| 316 | ||
| 317 |
# Roster-based analysis |
|
| 318 | 8x |
roster_analysis <- NULL |
| 319 | 8x |
if (include_roster_comparison) {
|
| 320 | 8x |
roster_analysis <- analyze_roster_attendance(batch_results) |
| 321 |
} |
|
| 322 | ||
| 323 |
# Create result |
|
| 324 | 8x |
result <- list( |
| 325 | 8x |
comparison_data = comparison_data, |
| 326 | 8x |
insights = insights, |
| 327 | 8x |
trends = trends, |
| 328 | 8x |
visualization_data = visualization_data, |
| 329 | 8x |
roster_analysis = roster_analysis |
| 330 |
) |
|
| 331 | ||
| 332 |
# Add attributes |
|
| 333 | 8x |
attr(result, "comparison_metrics") <- comparison_metrics |
| 334 | 8x |
attr(result, "visualization_included") <- visualization |
| 335 | ||
| 336 | 8x |
return(result) |
| 337 |
} |
|
| 338 | ||
| 339 |
#' Validate all ideal course scenarios |
|
| 340 |
#' |
|
| 341 |
#' Performs comprehensive validation of all ideal course scenarios, checking |
|
| 342 |
#' expected patterns, data consistency, and processing quality. |
|
| 343 |
#' |
|
| 344 |
#' @param batch_results Results from process_ideal_course_batch() |
|
| 345 |
#' @param validation_rules List. Custom validation rules. Default: NULL (uses defaults) |
|
| 346 |
#' @param detailed_report Logical. Whether to generate detailed report. |
|
| 347 |
#' Default: TRUE |
|
| 348 |
#' @param include_data_quality Logical. Whether to include data quality checks. |
|
| 349 |
#' Default: TRUE |
|
| 350 |
#' |
|
| 351 |
#' @return List containing validation results: |
|
| 352 |
#' - validation_summary: Overall validation status |
|
| 353 |
#' - rule_results: Results for each validation rule |
|
| 354 |
#' - data_quality_report: Data quality assessment (if include_data_quality = TRUE) |
|
| 355 |
#' - recommendations: Recommendations for improvement |
|
| 356 |
#' - detailed_report: Detailed validation report (if detailed_report = TRUE) |
|
| 357 |
#' |
|
| 358 |
#' @examples |
|
| 359 |
#' \dontrun{
|
|
| 360 |
#' # Process batch data first |
|
| 361 |
#' batch_results <- process_ideal_course_batch() |
|
| 362 |
#' |
|
| 363 |
#' # Validate scenarios |
|
| 364 |
#' validation <- validate_ideal_scenarios(batch_results) |
|
| 365 |
#' |
|
| 366 |
#' # Validate with custom rules |
|
| 367 |
#' custom_rules <- list( |
|
| 368 |
#' min_participants = 3, |
|
| 369 |
#' max_duration = 600 |
|
| 370 |
#' ) |
|
| 371 |
#' validation <- validate_ideal_scenarios(batch_results, validation_rules = custom_rules) |
|
| 372 |
#' } |
|
| 373 |
#' |
|
| 374 |
#' @export |
|
| 375 |
#' @keywords deprecated |
|
| 376 |
validate_ideal_scenarios <- function(batch_results = NULL, |
|
| 377 |
validation_rules = NULL, |
|
| 378 |
detailed_report = TRUE, |
|
| 379 |
include_data_quality = TRUE) {
|
|
| 380 |
# DEPRECATED: This function will be removed in the next version |
|
| 381 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 382 | 10x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 383 | ! |
warning("Function 'validate_ideal_scenarios' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 384 |
} |
|
| 385 | ||
| 386 |
# Set default validation rules if none provided |
|
| 387 | 10x |
if (is.null(validation_rules)) {
|
| 388 | 9x |
validation_rules <- list( |
| 389 | 9x |
min_sessions = 3, |
| 390 | 9x |
min_participants_per_session = 3, |
| 391 | 9x |
max_participants_per_session = 10, |
| 392 | 9x |
min_total_comments = 10, |
| 393 | 9x |
max_session_duration = 600, # 10 minutes |
| 394 | 9x |
require_name_consistency = TRUE, |
| 395 | 9x |
require_timestamp_consistency = TRUE, |
| 396 | 9x |
require_comment_content = TRUE |
| 397 |
) |
|
| 398 |
} |
|
| 399 | ||
| 400 |
# Initialize validation results |
|
| 401 | 10x |
rule_results <- list() |
| 402 | 10x |
validation_summary <- list( |
| 403 | 10x |
total_rules = length(validation_rules), |
| 404 | 10x |
passed_rules = 0, |
| 405 | 10x |
failed_rules = 0, |
| 406 | 10x |
overall_status = "PENDING" |
| 407 |
) |
|
| 408 | ||
| 409 |
# Extract data from batch results |
|
| 410 | 10x |
session_data <- batch_results$session_data |
| 411 | 10x |
summary_metrics <- batch_results$summary_metrics |
| 412 | 10x |
participation_patterns <- batch_results$participation_patterns |
| 413 | ||
| 414 |
# Validate number of sessions |
|
| 415 | 10x |
if ("min_sessions" %in% names(validation_rules)) {
|
| 416 | 10x |
rule_results$session_count <- validate_session_count( |
| 417 | 10x |
session_data, validation_rules$min_sessions |
| 418 |
) |
|
| 419 |
} |
|
| 420 | ||
| 421 |
# Validate participant counts |
|
| 422 | 10x |
if ("min_participants_per_session" %in% names(validation_rules) ||
|
| 423 | 10x |
"max_participants_per_session" %in% names(validation_rules)) {
|
| 424 | 10x |
rule_results$participant_counts <- validate_participant_counts( |
| 425 | 10x |
participation_patterns, validation_rules |
| 426 |
) |
|
| 427 |
} |
|
| 428 | ||
| 429 |
# Validate engagement metrics |
|
| 430 | 10x |
if ("min_total_comments" %in% names(validation_rules)) {
|
| 431 | 10x |
rule_results$engagement_metrics <- validate_engagement_metrics( |
| 432 | 10x |
summary_metrics, validation_rules$min_total_comments |
| 433 |
) |
|
| 434 |
} |
|
| 435 | ||
| 436 |
# Validate session duration |
|
| 437 | 10x |
if ("max_session_duration" %in% names(validation_rules)) {
|
| 438 | 10x |
rule_results$session_duration <- validate_session_duration( |
| 439 | 10x |
session_data, validation_rules$max_session_duration |
| 440 |
) |
|
| 441 |
} |
|
| 442 | ||
| 443 |
# Validate data consistency |
|
| 444 | 10x |
if ("require_name_consistency" %in% names(validation_rules) &&
|
| 445 | 10x |
validation_rules$require_name_consistency) {
|
| 446 | 9x |
rule_results$name_consistency <- validate_name_consistency(session_data) |
| 447 |
} |
|
| 448 | ||
| 449 | 10x |
if ("require_timestamp_consistency" %in% names(validation_rules) &&
|
| 450 | 10x |
validation_rules$require_timestamp_consistency) {
|
| 451 | 9x |
rule_results$timestamp_consistency <- validate_timestamp_consistency(session_data) |
| 452 |
} |
|
| 453 | ||
| 454 | 10x |
if ("require_comment_content" %in% names(validation_rules) &&
|
| 455 | 10x |
validation_rules$require_comment_content) {
|
| 456 | 9x |
rule_results$comment_content <- validate_comment_content(session_data) |
| 457 |
} |
|
| 458 | ||
| 459 |
# Calculate validation summary |
|
| 460 | 10x |
passed_rules <- sum(sapply(rule_results, function(x) {
|
| 461 | 67x |
if (is.list(x) && "status" %in% names(x)) {
|
| 462 | 19x |
x$status == "PASS" |
| 463 | 48x |
} else if (is.list(x)) {
|
| 464 |
# Handle nested lists (like participant_counts) |
|
| 465 | 48x |
sum(sapply(x, function(y) if (is.list(y) && "status" %in% names(y)) y$status == "PASS" else FALSE)) |
| 466 |
} else {
|
|
| 467 | ! |
FALSE |
| 468 |
} |
|
| 469 |
})) |
|
| 470 | ||
| 471 | 10x |
failed_rules <- sum(sapply(rule_results, function(x) {
|
| 472 | 67x |
if (is.list(x) && "status" %in% names(x)) {
|
| 473 | 19x |
x$status == "FAIL" |
| 474 | 48x |
} else if (is.list(x)) {
|
| 475 |
# Handle nested lists (like participant_counts) |
|
| 476 | 48x |
sum(sapply(x, function(y) if (is.list(y) && "status" %in% names(y)) y$status == "FAIL" else FALSE)) |
| 477 |
} else {
|
|
| 478 | ! |
FALSE |
| 479 |
} |
|
| 480 |
})) |
|
| 481 | ||
| 482 | 10x |
validation_summary$passed_rules <- passed_rules |
| 483 | 10x |
validation_summary$failed_rules <- failed_rules |
| 484 | 10x |
validation_summary$overall_status <- ifelse(failed_rules == 0, "PASS", "FAIL") |
| 485 | ||
| 486 |
# Generate data quality report |
|
| 487 | 10x |
data_quality_report <- NULL |
| 488 | 10x |
if (include_data_quality) {
|
| 489 | 9x |
data_quality_report <- generate_data_quality_report(session_data, summary_metrics) |
| 490 |
} |
|
| 491 | ||
| 492 |
# Generate recommendations |
|
| 493 | 10x |
recommendations <- generate_ideal_validation_recommendations(rule_results) |
| 494 | ||
| 495 |
# Generate detailed report |
|
| 496 | 10x |
detailed_report_content <- NULL |
| 497 | 10x |
if (detailed_report) {
|
| 498 |
# Create a results object compatible with the validation function |
|
| 499 | 9x |
validation_results <- list( |
| 500 | 9x |
validation_results = rule_results, |
| 501 | 9x |
overall_status = validation_summary$overall_status, |
| 502 | 9x |
summary = validation_summary, |
| 503 | 9x |
recommendations = recommendations, |
| 504 | 9x |
timestamp = Sys.time() |
| 505 |
) |
|
| 506 | 9x |
detailed_report_content <- generate_detailed_validation_report(validation_results) |
| 507 |
} |
|
| 508 | ||
| 509 |
# Create result |
|
| 510 | 10x |
result <- list( |
| 511 | 10x |
validation_summary = validation_summary, |
| 512 | 10x |
rule_results = rule_results, |
| 513 | 10x |
data_quality_report = data_quality_report, |
| 514 | 10x |
recommendations = recommendations, |
| 515 | 10x |
detailed_report = detailed_report_content |
| 516 |
) |
|
| 517 | ||
| 518 |
# Add attributes |
|
| 519 | 10x |
attr(result, "validation_timestamp") <- Sys.time() |
| 520 | 10x |
attr(result, "validation_rules_used") <- names(validation_rules) |
| 521 | ||
| 522 | 10x |
return(result) |
| 523 |
} |
|
| 524 | ||
| 525 |
# Helper functions for compare_ideal_sessions |
|
| 526 | ||
| 527 |
#' Generate comparison insights |
|
| 528 |
#' @keywords internal |
|
| 529 |
generate_comparison_insights <- function(comparison_data, metrics) {
|
|
| 530 |
# DEPRECATED: This function will be removed in the next version |
|
| 531 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 532 | 8x |
warning("Function 'generate_comparison_insights' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 533 | ||
| 534 | 8x |
insights <- list() |
| 535 | ||
| 536 | 8x |
if (nrow(comparison_data) < 2) {
|
| 537 | ! |
insights$general <- "Insufficient data for comparison (need at least 2 sessions)" |
| 538 | ! |
return(insights) |
| 539 |
} |
|
| 540 | ||
| 541 |
# General insights |
|
| 542 | 8x |
insights$general <- paste( |
| 543 | 8x |
"Analysis of", nrow(comparison_data), "ideal course sessions with", |
| 544 | 8x |
length(unique(comparison_data$total_participants)), "unique participant counts" |
| 545 |
) |
|
| 546 | ||
| 547 |
# Metric-specific insights |
|
| 548 | 8x |
if ("total_comments" %in% metrics) {
|
| 549 | 8x |
comment_range <- range(comparison_data$total_comments) |
| 550 | 8x |
insights$comments <- paste( |
| 551 | 8x |
"Total comments range from", comment_range[1], "to", comment_range[2], |
| 552 | 8x |
"across sessions" |
| 553 |
) |
|
| 554 |
} |
|
| 555 | ||
| 556 | 8x |
if ("duration" %in% metrics) {
|
| 557 | 7x |
duration_range <- range(comparison_data$total_duration) |
| 558 | 7x |
insights$duration <- paste( |
| 559 | 7x |
"Total duration ranges from", round(duration_range[1], 1), "to", |
| 560 | 7x |
round(duration_range[2], 1), "seconds across sessions" |
| 561 |
) |
|
| 562 |
} |
|
| 563 | ||
| 564 | 8x |
if ("wpm" %in% metrics) {
|
| 565 | 1x |
wpm_range <- range(comparison_data$avg_wpm, na.rm = TRUE) |
| 566 | 1x |
insights$wpm <- paste( |
| 567 | 1x |
"Average words per minute ranges from", round(wpm_range[1], 1), "to", |
| 568 | 1x |
round(wpm_range[2], 1), "across sessions" |
| 569 |
) |
|
| 570 |
} |
|
| 571 | ||
| 572 | 8x |
return(insights) |
| 573 |
} |
|
| 574 | ||
| 575 |
#' Analyze session trends |
|
| 576 |
#' @keywords internal |
|
| 577 |
analyze_session_trends <- function(comparison_data, metrics) {
|
|
| 578 |
# DEPRECATED: This function will be removed in the next version |
|
| 579 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 580 | 8x |
warning("Function 'analyze_session_trends' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 581 | ||
| 582 | 8x |
trends <- list() |
| 583 | ||
| 584 | 8x |
if (nrow(comparison_data) < 2) {
|
| 585 | ! |
trends$general <- "Insufficient data for trend analysis" |
| 586 | ! |
return(trends) |
| 587 |
} |
|
| 588 | ||
| 589 |
# Sort by session number |
|
| 590 | 8x |
comparison_data$session_num <- as.numeric(gsub("session", "", comparison_data$session))
|
| 591 | 8x |
comparison_data <- comparison_data[order(comparison_data$session_num), ] |
| 592 | ||
| 593 |
# Analyze trends for each metric |
|
| 594 | 8x |
for (metric in metrics) {
|
| 595 | 16x |
if (metric %in% names(comparison_data)) {
|
| 596 | 8x |
values <- comparison_data[[metric]] |
| 597 | 8x |
if (length(values) >= 2) {
|
| 598 |
# Simple trend detection |
|
| 599 | 8x |
first_half <- mean(values[1:ceiling(length(values) / 2)], na.rm = TRUE) |
| 600 | 8x |
second_half <- mean(values[ceiling(length(values) / 2):length(values)], na.rm = TRUE) |
| 601 | ||
| 602 | 8x |
if (second_half > first_half * 1.1) {
|
| 603 | ! |
trends[[metric]] <- "increasing" |
| 604 | 8x |
} else if (second_half < first_half * 0.9) {
|
| 605 | 1x |
trends[[metric]] <- "decreasing" |
| 606 |
} else {
|
|
| 607 | 7x |
trends[[metric]] <- "stable" |
| 608 |
} |
|
| 609 |
} |
|
| 610 |
} |
|
| 611 |
} |
|
| 612 | ||
| 613 | 8x |
return(trends) |
| 614 |
} |
|
| 615 | ||
| 616 |
#' Prepare visualization data |
|
| 617 |
#' @keywords internal |
|
| 618 |
prepare_visualization_data <- function(comparison_data, metrics) {
|
|
| 619 |
# DEPRECATED: This function will be removed in the next version |
|
| 620 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 621 | 7x |
warning("Function 'prepare_visualization_data' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 622 | ||
| 623 |
# Create long format data for plotting |
|
| 624 | 7x |
plot_data <- data.frame() |
| 625 | ||
| 626 | 7x |
for (metric in metrics) {
|
| 627 | 14x |
if (metric %in% names(comparison_data)) {
|
| 628 | 7x |
metric_data <- data.frame( |
| 629 | 7x |
session = comparison_data$session, |
| 630 | 7x |
metric = metric, |
| 631 | 7x |
value = comparison_data[[metric]], |
| 632 | 7x |
stringsAsFactors = FALSE |
| 633 |
) |
|
| 634 | 7x |
plot_data <- rbind(plot_data, metric_data) |
| 635 |
} |
|
| 636 |
} |
|
| 637 | ||
| 638 | 7x |
return(plot_data) |
| 639 |
} |
|
| 640 | ||
| 641 |
#' Analyze roster attendance |
|
| 642 |
#' @keywords internal |
|
| 643 |
analyze_roster_attendance <- function(batch_results) {
|
|
| 644 |
# DEPRECATED: This function will be removed in the next version |
|
| 645 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 646 | 8x |
warning("Function 'analyze_roster_attendance' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 647 | ||
| 648 |
# This would analyze attendance against the roster |
|
| 649 |
# For now, return basic participation patterns |
|
| 650 | 8x |
participation_patterns <- batch_results$participation_patterns |
| 651 | ||
| 652 | 8x |
attendance_summary <- list( |
| 653 | 8x |
total_unique_participants = length(unique(unlist(participation_patterns))), |
| 654 | 8x |
participants_per_session = sapply(participation_patterns, length), |
| 655 | 8x |
consistent_participants = length(Reduce(intersect, participation_patterns)) |
| 656 |
) |
|
| 657 | ||
| 658 | 8x |
return(attendance_summary) |
| 659 |
} |
|
| 660 | ||
| 661 |
# Helper functions for validate_ideal_scenarios |
|
| 662 | ||
| 663 |
#' Validate session count |
|
| 664 |
#' @keywords internal |
|
| 665 |
validate_session_count <- function(session_data, min_sessions) {
|
|
| 666 |
# DEPRECATED: This function will be removed in the next version |
|
| 667 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 668 | 10x |
warning("Function 'validate_session_count' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 669 | ||
| 670 | 10x |
actual_sessions <- length(session_data) |
| 671 | 10x |
status <- ifelse(actual_sessions >= min_sessions, "PASS", "FAIL") |
| 672 | ||
| 673 | 10x |
return(list( |
| 674 | 10x |
status = status, |
| 675 | 10x |
expected = min_sessions, |
| 676 | 10x |
actual = actual_sessions, |
| 677 | 10x |
message = paste("Expected at least", min_sessions, "sessions, found", actual_sessions)
|
| 678 |
)) |
|
| 679 |
} |
|
| 680 | ||
| 681 |
#' Validate participant counts |
|
| 682 |
#' @keywords internal |
|
| 683 |
validate_participant_counts <- function(participation_patterns, rules) {
|
|
| 684 |
# DEPRECATED: This function will be removed in the next version |
|
| 685 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 686 | 10x |
warning("Function 'validate_participant_counts' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 687 | ||
| 688 | 10x |
results <- list() |
| 689 | ||
| 690 | 10x |
for (session_name in names(participation_patterns)) {
|
| 691 | 30x |
participant_count <- length(participation_patterns[[session_name]]) |
| 692 | ||
| 693 | 30x |
min_check <- ifelse("min_participants_per_session" %in% names(rules),
|
| 694 | 30x |
participant_count >= rules$min_participants_per_session, TRUE |
| 695 |
) |
|
| 696 | 30x |
max_check <- ifelse("max_participants_per_session" %in% names(rules),
|
| 697 | 30x |
participant_count <= rules$max_participants_per_session, TRUE |
| 698 |
) |
|
| 699 | ||
| 700 | 30x |
status <- ifelse(min_check && max_check, "PASS", "FAIL") |
| 701 | ||
| 702 | 30x |
results[[session_name]] <- list( |
| 703 | 30x |
status = status, |
| 704 | 30x |
participant_count = participant_count, |
| 705 | 30x |
min_expected = rules$min_participants_per_session %||% 0, |
| 706 | 30x |
max_expected = rules$max_participants_per_session %||% Inf |
| 707 |
) |
|
| 708 |
} |
|
| 709 | ||
| 710 | 10x |
return(results) |
| 711 |
} |
|
| 712 | ||
| 713 |
#' Validate engagement metrics |
|
| 714 |
#' @keywords internal |
|
| 715 |
validate_engagement_metrics <- function(summary_metrics, min_comments) {
|
|
| 716 |
# DEPRECATED: This function will be removed in the next version |
|
| 717 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 718 | 10x |
warning("Function 'validate_engagement_metrics' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 719 | ||
| 720 | 10x |
results <- list() |
| 721 | ||
| 722 | 10x |
for (session_name in names(summary_metrics)) {
|
| 723 | 30x |
session_metrics <- summary_metrics[[session_name]] |
| 724 | 30x |
if (!is.null(session_metrics) && nrow(session_metrics) > 0) {
|
| 725 | 30x |
total_comments <- sum(session_metrics$n, na.rm = TRUE) |
| 726 | 30x |
status <- ifelse(total_comments >= min_comments, "PASS", "FAIL") |
| 727 | ||
| 728 | 30x |
results[[session_name]] <- list( |
| 729 | 30x |
status = status, |
| 730 | 30x |
total_comments = total_comments, |
| 731 | 30x |
expected_min = min_comments |
| 732 |
) |
|
| 733 |
} |
|
| 734 |
} |
|
| 735 | ||
| 736 | 10x |
return(results) |
| 737 |
} |
|
| 738 | ||
| 739 |
#' Validate session duration |
|
| 740 |
#' @keywords internal |
|
| 741 |
validate_session_duration <- function(session_data, max_duration) {
|
|
| 742 |
# DEPRECATED: This function will be removed in the next version |
|
| 743 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 744 | 10x |
warning("Function 'validate_session_duration' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 745 | ||
| 746 | 10x |
results <- list() |
| 747 | ||
| 748 | 10x |
for (session_name in names(session_data)) {
|
| 749 | 30x |
session <- session_data[[session_name]] |
| 750 | 30x |
if (!is.null(session) && nrow(session) > 0) {
|
| 751 | 30x |
total_duration <- sum(session$duration, na.rm = TRUE) |
| 752 | 30x |
status <- ifelse(total_duration <= max_duration, "PASS", "FAIL") |
| 753 | ||
| 754 | 30x |
results[[session_name]] <- list( |
| 755 | 30x |
status = status, |
| 756 | 30x |
total_duration = total_duration, |
| 757 | 30x |
max_expected = max_duration |
| 758 |
) |
|
| 759 |
} |
|
| 760 |
} |
|
| 761 | ||
| 762 | 10x |
return(results) |
| 763 |
} |
|
| 764 | ||
| 765 |
#' Validate name consistency |
|
| 766 |
#' @keywords internal |
|
| 767 |
validate_name_consistency <- function(session_data) {
|
|
| 768 |
# DEPRECATED: This function will be removed in the next version |
|
| 769 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 770 | 9x |
warning("Function 'validate_name_consistency' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 771 | ||
| 772 | 9x |
all_names <- unique(unlist(lapply(session_data, function(x) {
|
| 773 | ! |
if (!is.null(x) && nrow(x) > 0) unique(x$name) else character(0) |
| 774 |
}))) |
|
| 775 | ||
| 776 |
# Check for consistent name formats (basic check) |
|
| 777 | 9x |
has_consistent_format <- all(grepl("^[A-Za-z]+", all_names))
|
| 778 | ||
| 779 | 9x |
return(list( |
| 780 | 9x |
status = ifelse(has_consistent_format, "PASS", "FAIL"), |
| 781 | 9x |
unique_names = length(all_names), |
| 782 | 9x |
has_consistent_format = has_consistent_format |
| 783 |
)) |
|
| 784 |
} |
|
| 785 | ||
| 786 |
#' Validate timestamp consistency |
|
| 787 |
#' @keywords internal |
|
| 788 |
validate_timestamp_consistency <- function(session_data) {
|
|
| 789 |
# DEPRECATED: This function will be removed in the next version |
|
| 790 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 791 | 9x |
warning("Function 'validate_timestamp_consistency' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 792 | ||
| 793 | 9x |
results <- list() |
| 794 | ||
| 795 | 9x |
for (session_name in names(session_data)) {
|
| 796 | 27x |
session <- session_data[[session_name]] |
| 797 | 27x |
if (!is.null(session) && nrow(session) > 0) {
|
| 798 |
# Check for valid timestamps |
|
| 799 | 27x |
valid_timestamps <- all(!is.na(session$start) & !is.na(session$end)) |
| 800 | 27x |
chronological_order <- all(diff(as.numeric(session$start)) >= 0, na.rm = TRUE) |
| 801 | ||
| 802 | 27x |
status <- ifelse(valid_timestamps && chronological_order, "PASS", "FAIL") |
| 803 | ||
| 804 | 27x |
results[[session_name]] <- list( |
| 805 | 27x |
status = status, |
| 806 | 27x |
valid_timestamps = valid_timestamps, |
| 807 | 27x |
chronological_order = chronological_order |
| 808 |
) |
|
| 809 |
} |
|
| 810 |
} |
|
| 811 | ||
| 812 | 9x |
return(results) |
| 813 |
} |
|
| 814 | ||
| 815 |
#' Validate comment content |
|
| 816 |
#' @keywords internal |
|
| 817 |
validate_comment_content <- function(session_data) {
|
|
| 818 |
# DEPRECATED: This function will be removed in the next version |
|
| 819 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 820 | 9x |
warning("Function 'validate_comment_content' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 821 | ||
| 822 | 9x |
results <- list() |
| 823 | ||
| 824 | 9x |
for (session_name in names(session_data)) {
|
| 825 | 27x |
session <- session_data[[session_name]] |
| 826 | 27x |
if (!is.null(session) && nrow(session) > 0) {
|
| 827 |
# Check for non-empty comments |
|
| 828 | 27x |
non_empty_comments <- sum(!is.na(session$comment) & session$comment != "", na.rm = TRUE) |
| 829 | 27x |
total_comments <- nrow(session) |
| 830 | 27x |
content_ratio <- non_empty_comments / total_comments |
| 831 | ||
| 832 | 27x |
status <- ifelse(content_ratio > 0.8, "PASS", "FAIL") |
| 833 | ||
| 834 | 27x |
results[[session_name]] <- list( |
| 835 | 27x |
status = status, |
| 836 | 27x |
non_empty_comments = non_empty_comments, |
| 837 | 27x |
total_comments = total_comments, |
| 838 | 27x |
content_ratio = content_ratio |
| 839 |
) |
|
| 840 |
} |
|
| 841 |
} |
|
| 842 | ||
| 843 | 9x |
return(results) |
| 844 |
} |
|
| 845 | ||
| 846 |
#' Generate data quality report |
|
| 847 |
#' @keywords internal |
|
| 848 |
generate_data_quality_report <- function(session_data, summary_metrics) {
|
|
| 849 |
# DEPRECATED: This function will be removed in the next version |
|
| 850 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 851 | 9x |
warning("Function 'generate_data_quality_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 852 | ||
| 853 | 9x |
report <- list() |
| 854 | ||
| 855 |
# Overall statistics |
|
| 856 | 9x |
total_sessions <- length(session_data) |
| 857 | 9x |
total_participants <- length(unique(unlist(lapply(session_data, function(x) {
|
| 858 | ! |
if (!is.null(x) && nrow(x) > 0) unique(x$name) else character(0) |
| 859 |
})))) |
|
| 860 | ||
| 861 | 9x |
report$overall_stats <- list( |
| 862 | 9x |
total_sessions = total_sessions, |
| 863 | 9x |
total_participants = total_participants, |
| 864 | 9x |
avg_participants_per_session = total_participants / total_sessions |
| 865 |
) |
|
| 866 | ||
| 867 |
# Session-specific quality metrics |
|
| 868 | 9x |
session_quality <- list() |
| 869 | 9x |
for (session_name in names(session_data)) {
|
| 870 | 27x |
session <- session_data[[session_name]] |
| 871 | 27x |
if (!is.null(session) && nrow(session) > 0) {
|
| 872 | 27x |
session_quality[[session_name]] <- list( |
| 873 | 27x |
total_rows = nrow(session), |
| 874 | 27x |
unique_participants = length(unique(session$name)), |
| 875 | 27x |
missing_names = sum(is.na(session$name)), |
| 876 | 27x |
missing_comments = sum(is.na(session$comment) | session$comment == ""), |
| 877 | 27x |
missing_timestamps = sum(is.na(session$start) | is.na(session$end)) |
| 878 |
) |
|
| 879 |
} |
|
| 880 |
} |
|
| 881 | ||
| 882 | 9x |
report$session_quality <- session_quality |
| 883 | ||
| 884 | 9x |
return(report) |
| 885 |
} |
|
| 886 | ||
| 887 |
#' Generate ideal validation recommendations |
|
| 888 |
#' @keywords internal |
|
| 889 |
generate_ideal_validation_recommendations <- function(rule_results) {
|
|
| 890 |
# DEPRECATED: This function will be removed in the next version |
|
| 891 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 892 | 10x |
warning("Function 'generate_validation_recommendations' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 893 | ||
| 894 | 10x |
recommendations <- list() |
| 895 | ||
| 896 |
# Generate specific recommendations based on failed rules |
|
| 897 |
# Handle both simple and nested rule structures |
|
| 898 | 10x |
failed_rules <- character(0) |
| 899 | 10x |
for (rule_name in names(rule_results)) {
|
| 900 | 67x |
rule_result <- rule_results[[rule_name]] |
| 901 | 67x |
if (is.list(rule_result) && "status" %in% names(rule_result)) {
|
| 902 | 19x |
if (rule_result$status == "FAIL") {
|
| 903 | ! |
failed_rules <- c(failed_rules, rule_name) |
| 904 |
} |
|
| 905 | 48x |
} else if (is.list(rule_result)) {
|
| 906 |
# Handle nested lists |
|
| 907 | 48x |
for (nested_name in names(rule_result)) {
|
| 908 | 144x |
nested_result <- rule_result[[nested_name]] |
| 909 | 144x |
if (is.list(nested_result) && "status" %in% names(nested_result)) {
|
| 910 | 144x |
if (nested_result$status == "FAIL") {
|
| 911 | 33x |
failed_rules <- c(failed_rules, paste0(rule_name, ".", nested_name)) |
| 912 |
} |
|
| 913 |
} |
|
| 914 |
} |
|
| 915 |
} |
|
| 916 |
} |
|
| 917 | ||
| 918 | 10x |
if (length(failed_rules) > 0) {
|
| 919 | 9x |
recommendations$priority <- "Address failed validation rules" |
| 920 | ||
| 921 | 9x |
if ("session_count" %in% failed_rules) {
|
| 922 | ! |
recommendations$session_count <- "Ensure all ideal course sessions are available" |
| 923 |
} |
|
| 924 | ||
| 925 | 9x |
if (any(grepl("participant_counts", failed_rules))) {
|
| 926 | ! |
recommendations$participant_counts <- "Review participant count expectations" |
| 927 |
} |
|
| 928 | ||
| 929 | 9x |
if (any(grepl("engagement_metrics", failed_rules))) {
|
| 930 | 9x |
recommendations$engagement_metrics <- "Check minimum engagement thresholds" |
| 931 |
} |
|
| 932 |
} else {
|
|
| 933 | 1x |
recommendations$priority <- "All validation rules passed" |
| 934 | 1x |
recommendations$next_steps <- "Proceed with confidence in data quality" |
| 935 |
} |
|
| 936 | ||
| 937 | 10x |
return(recommendations) |
| 938 |
} |
| 1 |
#' Plot Users by Metric |
|
| 2 |
#' |
|
| 3 |
#' Deprecated: use `plot_users(mask_by = 'rank')` instead. Delegates to `plot_users()` |
|
| 4 |
#' for backward compatibility. |
|
| 5 |
#' |
|
| 6 |
#' @param df a tibble that summarizes results at the level of the class section and student. |
|
| 7 |
#' @param metric Label of the metric to plot. Defaults to 'session_ct'. |
|
| 8 |
#' |
|
| 9 |
#' @return A ggplot object. |
|
| 10 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 11 |
plot_users_masked_section_by_metric <- function(df = NULL, metric = "session_ct") {
|
|
| 12 |
# DEPRECATED: This function will be removed in the next version |
|
| 13 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 14 | 3x |
warning("Function 'plot_users_masked_section_by_metric' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 15 | ||
| 16 | ! |
if (!tibble::is_tibble(df)) stop("`df` must be a tibble")
|
| 17 | 3x |
if (!metric %in% names(df)) {
|
| 18 | 1x |
stop(sprintf("Metric '%s' not found in data", metric))
|
| 19 |
} |
|
| 20 | 2x |
plot_users( |
| 21 | 2x |
data = df, |
| 22 | 2x |
metric = metric, |
| 23 | 2x |
student_col = "preferred_name", |
| 24 | 2x |
facet_by = "section", |
| 25 | 2x |
mask_by = "rank" |
| 26 |
) |
|
| 27 |
} |
| 1 |
#' Analyze Multi-Session Attendance Patterns |
|
| 2 |
#' |
|
| 3 |
#' Analyzes attendance patterns across multiple Zoom sessions, tracking who attended |
|
| 4 |
#' which sessions and identifying participation patterns while maintaining privacy compliance. |
|
| 5 |
#' |
|
| 6 |
#' @param transcript_files Character vector of transcript file paths |
|
| 7 |
#' @param roster_data Data frame containing student roster information |
|
| 8 |
#' @param data_folder Path to the data folder containing transcripts |
|
| 9 |
#' @param transcripts_folder Name of the transcripts subfolder (default: "transcripts") |
|
| 10 |
#' @param unmatched_names_action Action for unmatched names: "stop" (default) or "warn" |
|
| 11 |
#' @param privacy_level Privacy level for output masking: "ferpa_strict", |
|
| 12 |
#' "ferpa_standard", "mask", "none" |
|
| 13 |
#' @param min_attendance_threshold Minimum attendance percentage to be considered a |
|
| 14 |
#' "consistent attendee" (default: 0.5) |
|
| 15 |
#' |
|
| 16 |
#' @return A list containing: |
|
| 17 |
#' - `attendance_matrix`: Data frame with participants as rows and sessions as columns |
|
| 18 |
#' - `attendance_summary`: Summary statistics for each participant |
|
| 19 |
#' - `session_summary`: Summary statistics for each session |
|
| 20 |
#' - `participation_patterns`: Analysis of participation patterns |
|
| 21 |
#' - `privacy_compliant`: Boolean indicating if all outputs maintain privacy |
|
| 22 |
#' |
|
| 23 |
#' @examples |
|
| 24 |
#' \dontrun{
|
|
| 25 |
#' # Analyze attendance across multiple sessions |
|
| 26 |
#' transcript_files <- c("session1.vtt", "session2.vtt", "session3.vtt")
|
|
| 27 |
#' roster_data <- load_roster(data_folder = "data/metadata", roster_file = "roster.csv") |
|
| 28 |
#' |
|
| 29 |
#' results <- analyze_multi_session_attendance( |
|
| 30 |
#' transcript_files = transcript_files, |
|
| 31 |
#' roster_data = roster_data, |
|
| 32 |
#' data_folder = "data", |
|
| 33 |
#' unmatched_names_action = "warn" |
|
| 34 |
#' ) |
|
| 35 |
#' |
|
| 36 |
#' # View attendance summary |
|
| 37 |
#' print(results$attendance_summary) |
|
| 38 |
#' } |
|
| 39 |
#' |
|
| 40 |
#' @export |
|
| 41 |
analyze_multi_session_attendance <- function( |
|
| 42 |
transcript_files = NULL, |
|
| 43 |
roster_data = NULL, |
|
| 44 |
data_folder = ".", |
|
| 45 |
transcripts_folder = "transcripts", |
|
| 46 |
unmatched_names_action = c("stop", "warn"),
|
|
| 47 |
privacy_level = c("ferpa_strict", "ferpa_standard", "mask", "none"),
|
|
| 48 |
min_attendance_threshold = 0.5) {
|
|
| 49 |
# Validate inputs |
|
| 50 | 5x |
unmatched_names_action <- match.arg(unmatched_names_action) |
| 51 | 5x |
privacy_level <- match.arg(privacy_level) |
| 52 | ||
| 53 | 5x |
if (length(transcript_files) < 2) {
|
| 54 | 1x |
stop("At least 2 transcript files are required for multi-session analysis")
|
| 55 |
} |
|
| 56 | ||
| 57 | 4x |
if (!is.data.frame(roster_data) || nrow(roster_data) == 0) {
|
| 58 | 1x |
stop("roster_data must be a non-empty data frame")
|
| 59 |
} |
|
| 60 | ||
| 61 | 3x |
if (min_attendance_threshold < 0 || min_attendance_threshold > 1) {
|
| 62 | 1x |
stop("min_attendance_threshold must be between 0 and 1")
|
| 63 |
} |
|
| 64 | ||
| 65 |
# Set privacy defaults |
|
| 66 | 2x |
set_privacy_defaults( |
| 67 | 2x |
privacy_level = privacy_level, |
| 68 | 2x |
unmatched_names_action = unmatched_names_action |
| 69 |
) |
|
| 70 | ||
| 71 |
# Initialize tracking variables |
|
| 72 | 2x |
session_attendance <- list() |
| 73 | 2x |
session_metrics <- list() |
| 74 | 2x |
all_participants <- character(0) |
| 75 | 2x |
session_names <- character(0) |
| 76 | ||
| 77 |
# Process each session |
|
| 78 | 2x |
for (i in seq_along(transcript_files)) {
|
| 79 | 4x |
transcript_file <- transcript_files[i] |
| 80 | ||
| 81 |
# Construct full path if needed |
|
| 82 | 4x |
if (!file.exists(transcript_file)) {
|
| 83 | 4x |
full_path <- file.path(data_folder, transcripts_folder, transcript_file) |
| 84 | 4x |
if (!file.exists(full_path)) {
|
| 85 | ! |
warning(sprintf("Transcript file not found: %s", transcript_file))
|
| 86 | ! |
next |
| 87 |
} |
|
| 88 | 4x |
transcript_file <- full_path |
| 89 |
} |
|
| 90 | ||
| 91 | 4x |
session_name <- tools::file_path_sans_ext(basename(transcript_file)) |
| 92 | 4x |
session_names[i] <- session_name |
| 93 | ||
| 94 |
# Process transcript with privacy-aware name matching |
|
| 95 | 4x |
tryCatch( |
| 96 |
{
|
|
| 97 |
# Load transcript data first |
|
| 98 | 4x |
transcript_data <- load_zoom_transcript(transcript_file) |
| 99 | ||
| 100 |
# Process with privacy-aware name matching |
|
| 101 | 4x |
session_data <- process_transcript_with_privacy( |
| 102 | 4x |
transcript_data = transcript_data, |
| 103 | 4x |
roster_data = roster_data |
| 104 |
) |
|
| 105 | ||
| 106 |
# Extract participants for this session |
|
| 107 | 4x |
session_participants <- unique(session_data$name) |
| 108 | 4x |
session_attendance[[session_name]] <- session_participants |
| 109 | 4x |
all_participants <- unique(c(all_participants, session_participants)) |
| 110 | ||
| 111 |
# Calculate session metrics |
|
| 112 | 4x |
session_metrics[[session_name]] <- summarize_transcript_metrics( |
| 113 | 4x |
transcript_file_path = transcript_file, |
| 114 | 4x |
names_exclude = c("dead_air")
|
| 115 |
) |
|
| 116 |
}, |
|
| 117 | 4x |
error = function(e) {
|
| 118 | ! |
warning(sprintf("Error processing session %s: %s", session_name, e$message))
|
| 119 |
} |
|
| 120 |
) |
|
| 121 |
} |
|
| 122 | ||
| 123 | 2x |
if (length(all_participants) == 0) {
|
| 124 | ! |
stop("No participants found across all sessions")
|
| 125 |
} |
|
| 126 | ||
| 127 |
# Create attendance matrix |
|
| 128 | 2x |
attendance_matrix <- data.frame( |
| 129 | 2x |
participant = all_participants, |
| 130 | 2x |
stringsAsFactors = FALSE |
| 131 |
) |
|
| 132 | ||
| 133 | 2x |
for (session_name in names(session_attendance)) {
|
| 134 | 4x |
attendance_matrix[[session_name]] <- all_participants %in% session_attendance[[session_name]] |
| 135 |
} |
|
| 136 | ||
| 137 |
# Calculate attendance statistics |
|
| 138 | 2x |
total_sessions <- length(session_attendance) |
| 139 | 2x |
attendance_counts <- rowSums(attendance_matrix[, -1, drop = FALSE]) |
| 140 | 2x |
attendance_rates <- attendance_counts / total_sessions |
| 141 | ||
| 142 |
# Create attendance summary |
|
| 143 | 2x |
attendance_summary <- data.frame( |
| 144 | 2x |
participant = all_participants, |
| 145 | 2x |
total_sessions = attendance_counts, |
| 146 | 2x |
attendance_rate = round(attendance_rates * 100, 1), |
| 147 | 2x |
is_consistent_attendee = attendance_rates >= min_attendance_threshold, |
| 148 | 2x |
is_one_time_attendee = attendance_counts == 1, |
| 149 | 2x |
stringsAsFactors = FALSE |
| 150 |
) |
|
| 151 | ||
| 152 |
# Create session summary |
|
| 153 | 2x |
session_summary <- data.frame( |
| 154 | 2x |
session = names(session_attendance), |
| 155 | 2x |
participants = sapply(session_attendance, length), |
| 156 | 2x |
stringsAsFactors = FALSE |
| 157 |
) |
|
| 158 | ||
| 159 |
# Analyze participation patterns |
|
| 160 | 2x |
consistent_attendees <- all_participants[attendance_rates >= min_attendance_threshold] |
| 161 | 2x |
one_time_attendees <- all_participants[attendance_counts == 1] |
| 162 | 2x |
occasional_attendees <- all_participants[ |
| 163 | 2x |
attendance_counts > 1 & attendance_rates < min_attendance_threshold |
| 164 |
] |
|
| 165 | ||
| 166 | 2x |
participation_patterns <- list( |
| 167 | 2x |
total_participants = length(all_participants), |
| 168 | 2x |
total_sessions = total_sessions, |
| 169 | 2x |
consistent_attendees = length(consistent_attendees), |
| 170 | 2x |
occasional_attendees = length(occasional_attendees), |
| 171 | 2x |
one_time_attendees = length(one_time_attendees), |
| 172 | 2x |
average_attendance_rate = round(mean(attendance_rates) * 100, 1), |
| 173 | 2x |
median_attendance_rate = round(stats::median(attendance_rates) * 100, 1), |
| 174 | 2x |
attendance_rate_std = round(stats::sd(attendance_rates) * 100, 1) |
| 175 |
) |
|
| 176 | ||
| 177 |
# Validate privacy compliance |
|
| 178 | 2x |
privacy_compliant <- TRUE |
| 179 | 2x |
tryCatch( |
| 180 |
{
|
|
| 181 | 2x |
validate_privacy_compliance(attendance_summary, privacy_level = privacy_level) |
| 182 | 2x |
validate_privacy_compliance(session_summary, privacy_level = privacy_level) |
| 183 |
}, |
|
| 184 | 2x |
error = function(e) {
|
| 185 | ! |
privacy_compliant <<- FALSE |
| 186 | ! |
warning(sprintf("Privacy violation detected: %s", e$message))
|
| 187 |
} |
|
| 188 |
) |
|
| 189 | ||
| 190 |
# Return results |
|
| 191 | 2x |
result <- list( |
| 192 | 2x |
attendance_matrix = attendance_matrix, |
| 193 | 2x |
attendance_summary = attendance_summary, |
| 194 | 2x |
session_summary = session_summary, |
| 195 | 2x |
participation_patterns = participation_patterns, |
| 196 | 2x |
privacy_compliant = privacy_compliant, |
| 197 | 2x |
session_metrics = session_metrics |
| 198 |
) |
|
| 199 | ||
| 200 |
# Add privacy masking to sensitive data |
|
| 201 | 2x |
if (privacy_level != "none") {
|
| 202 | 2x |
result$attendance_matrix <- ensure_privacy( |
| 203 | 2x |
result$attendance_matrix, |
| 204 | 2x |
privacy_level = privacy_level |
| 205 |
) |
|
| 206 | 2x |
result$attendance_summary <- ensure_privacy( |
| 207 | 2x |
result$attendance_summary, |
| 208 | 2x |
privacy_level = privacy_level |
| 209 |
) |
|
| 210 | 2x |
result$session_summary <- ensure_privacy( |
| 211 | 2x |
result$session_summary, |
| 212 | 2x |
privacy_level = privacy_level |
| 213 |
) |
|
| 214 |
} |
|
| 215 | ||
| 216 | 2x |
return(result) |
| 217 |
} |
|
| 218 | ||
| 219 |
#' Generate Multi-Session Attendance Report |
|
| 220 |
#' |
|
| 221 |
#' Creates a comprehensive report of multi-session attendance analysis. |
|
| 222 |
#' |
|
| 223 |
#' @param analysis_results Results from `analyze_multi_session_attendance()` |
|
| 224 |
#' @param output_file Path to save the report (optional) |
|
| 225 |
#' @param include_charts Whether to include attendance charts (requires ggplot2) |
|
| 226 |
#' |
|
| 227 |
#' @return Character vector containing the report content |
|
| 228 |
#' |
|
| 229 |
#' @examples |
|
| 230 |
#' \dontrun{
|
|
| 231 |
#' results <- analyze_multi_session_attendance(transcript_files, roster_data) |
|
| 232 |
#' report <- generate_attendance_report(results, "attendance_report.md") |
|
| 233 |
#' } |
|
| 234 |
#' |
|
| 235 |
#' @export |
|
| 236 |
generate_attendance_report <- function( |
|
| 237 |
analysis_results = NULL, |
|
| 238 |
output_file = NULL, |
|
| 239 |
include_charts = FALSE) {
|
|
| 240 | 1x |
if (!is.list(analysis_results) || !"participation_patterns" %in% names(analysis_results)) {
|
| 241 | ! |
stop("analysis_results must be the output from analyze_multi_session_attendance()")
|
| 242 |
} |
|
| 243 | ||
| 244 | 1x |
patterns <- analysis_results$participation_patterns |
| 245 | 1x |
summary <- analysis_results$attendance_summary |
| 246 | ||
| 247 |
# Generate report content |
|
| 248 | 1x |
report_content <- c( |
| 249 | 1x |
"# Multi-Session Attendance Analysis Report", |
| 250 |
"", |
|
| 251 | 1x |
paste("**Generated**:", format(Sys.time(), "%Y-%m-%d %H:%M:%S")),
|
| 252 | 1x |
paste("**Sessions Analyzed**:", patterns$total_sessions),
|
| 253 | 1x |
paste("**Total Participants**:", patterns$total_participants),
|
| 254 |
"", |
|
| 255 | 1x |
"## Participation Summary", |
| 256 |
"", |
|
| 257 | 1x |
paste( |
| 258 | 1x |
"- **Consistent Attendees** (>=", patterns$total_sessions * 0.5, |
| 259 | 1x |
"sessions):", patterns$consistent_attendees |
| 260 |
), |
|
| 261 | 1x |
paste( |
| 262 | 1x |
"- **Occasional Attendees** (2-", |
| 263 | 1x |
ceiling(patterns$total_sessions * 0.5) - 1, |
| 264 | 1x |
"sessions):", patterns$occasional_attendees |
| 265 |
), |
|
| 266 | 1x |
paste("- **One-time Attendees**:", patterns$one_time_attendees),
|
| 267 |
"", |
|
| 268 | 1x |
"## Attendance Statistics", |
| 269 |
"", |
|
| 270 | 1x |
paste("- **Average Attendance Rate**:", patterns$average_attendance_rate, "%"),
|
| 271 | 1x |
paste("- **Median Attendance Rate**:", patterns$median_attendance_rate, "%"),
|
| 272 | 1x |
paste("- **Attendance Rate Std Dev**:", patterns$attendance_rate_std, "%"),
|
| 273 |
"", |
|
| 274 | 1x |
"## Privacy Compliance", |
| 275 |
"", |
|
| 276 | 1x |
if (analysis_results$privacy_compliant) {
|
| 277 | 1x |
"[PASS] All outputs maintain privacy compliance" |
| 278 |
} else {
|
|
| 279 | ! |
"[FAIL] Privacy violations detected" |
| 280 |
}, |
|
| 281 |
"" |
|
| 282 |
) |
|
| 283 | ||
| 284 |
# Add attendance matrix if privacy allows |
|
| 285 | 1x |
if (analysis_results$privacy_compliant) {
|
| 286 | 1x |
report_content <- c( |
| 287 | 1x |
report_content, |
| 288 | 1x |
"## Attendance Matrix", |
| 289 |
"", |
|
| 290 | 1x |
"| Participant | Sessions Attended | Attendance Rate |", |
| 291 |
"|-------------|-------------------|-----------------|" |
|
| 292 |
) |
|
| 293 | ||
| 294 | 1x |
for (i in seq_len(min(10, nrow(summary)))) { # Limit to first 10 for report
|
| 295 | 3x |
row <- summary[i, ] |
| 296 | 3x |
report_content <- c( |
| 297 | 3x |
report_content, |
| 298 | 3x |
sprintf( |
| 299 | 3x |
"| %s | %d | %.1f%% |", |
| 300 | 3x |
row$participant, |
| 301 | 3x |
row$total_sessions, |
| 302 | 3x |
row$attendance_rate |
| 303 |
) |
|
| 304 |
) |
|
| 305 |
} |
|
| 306 | ||
| 307 | 1x |
if (nrow(summary) > 10) {
|
| 308 | ! |
report_content <- c(report_content, "| ... | ... | ... |") |
| 309 |
} |
|
| 310 |
} |
|
| 311 | ||
| 312 |
# Save report if output file specified |
|
| 313 | 1x |
if (!is.null(output_file)) {
|
| 314 | ! |
writeLines(report_content, output_file) |
| 315 |
} |
|
| 316 | ||
| 317 | 1x |
return(report_content) |
| 318 |
} |
| 1 |
#' Write Engagement Metrics to CSV |
|
| 2 |
#' |
|
| 3 |
#' Deprecated: use `write_metrics(data, what = 'engagement', path = ...)` instead. |
|
| 4 |
#' |
|
| 5 |
#' @param metrics_data A tibble containing engagement metrics (typically from `summarize_transcript_files`) |
|
| 6 |
#' @param file_path Path where the CSV file should be saved |
|
| 7 |
#' @param comments_format How to format the comments column: "text" (semicolon-separated) or "count" (number of comments) |
|
| 8 |
#' |
|
| 9 |
#' @return Invisibly returns the processed data that was written |
|
| 10 |
#' @export |
|
| 11 |
#' @keywords deprecated |
|
| 12 |
write_engagement_metrics <- function(metrics_data = NULL, file_path = NULL, comments_format = c("text", "count")) {
|
|
| 13 |
# DEPRECATED: This function will be removed in the next version |
|
| 14 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 15 | 16x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 16 | ! |
warning("Function 'write_engagement_metrics' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 17 |
} |
|
| 18 | ||
| 19 | 16x |
comments_format <- match.arg(comments_format) |
| 20 | 15x |
write_metrics( |
| 21 | 15x |
data = metrics_data, |
| 22 | 15x |
what = "engagement", |
| 23 | 15x |
path = file_path, |
| 24 | 15x |
comments_format = comments_format |
| 25 |
) |
|
| 26 |
} |
| 1 |
#' Validation System |
|
| 2 |
#' |
|
| 3 |
#' @description Validates function audit and categorization results |
|
| 4 |
#' @keywords internal |
|
| 5 |
#' @noRd |
|
| 6 | ||
| 7 |
#' Validate function audit results |
|
| 8 |
#' |
|
| 9 |
#' @param function_categories Function categories from audit |
|
| 10 |
#' @param cran_functions Functions selected for CRAN |
|
| 11 |
#' @param function_analysis Function analysis results |
|
| 12 |
#' @return Validation results |
|
| 13 |
validate_audit_results <- function(function_categories, cran_functions, function_analysis) {
|
|
| 14 | ! |
cat("ā
Validating function audit results...\n")
|
| 15 | ||
| 16 | ! |
validation_results <- list( |
| 17 | ! |
function_count = length(cran_functions), |
| 18 | ! |
category_completeness = validate_categories(function_categories), |
| 19 | ! |
dependency_check = validate_dependencies(cran_functions, function_analysis), |
| 20 | ! |
documentation_check = validate_documentation(cran_functions, function_analysis), |
| 21 | ! |
test_coverage = validate_test_coverage(cran_functions, function_analysis), |
| 22 | ! |
cran_compliance = validate_cran_compliance(cran_functions, function_analysis) |
| 23 |
) |
|
| 24 | ||
| 25 |
# Print validation summary |
|
| 26 | ! |
print_validation_summary(validation_results) |
| 27 | ||
| 28 | ! |
return(validation_results) |
| 29 |
} |
|
| 30 | ||
| 31 |
#' Validate function categories |
|
| 32 |
#' |
|
| 33 |
#' @param function_categories Function categories from audit |
|
| 34 |
#' @return Category validation results |
|
| 35 |
validate_categories <- function(function_categories) {
|
|
| 36 |
# Check that all functions are categorized |
|
| 37 | ! |
all_functions <- unlist(function_categories) |
| 38 | ! |
total_functions <- length(all_functions) |
| 39 | ||
| 40 |
# Check for duplicates |
|
| 41 | ! |
duplicates <- any(duplicated(all_functions)) |
| 42 | ||
| 43 |
# Check for empty categories |
|
| 44 | ! |
empty_categories <- names(function_categories)[sapply(function_categories, length) == 0] |
| 45 | ||
| 46 |
# Check category distribution |
|
| 47 | ! |
category_distribution <- sapply(function_categories, length) |
| 48 | ! |
has_balanced_distribution <- all(category_distribution > 0) && |
| 49 | ! |
max(category_distribution) / min(category_distribution[category_distribution > 0]) < 10 |
| 50 | ||
| 51 | ! |
return(list( |
| 52 | ! |
total_functions = total_functions, |
| 53 | ! |
has_duplicates = duplicates, |
| 54 | ! |
categories_complete = total_functions > 0, |
| 55 | ! |
empty_categories = empty_categories, |
| 56 | ! |
has_balanced_distribution = has_balanced_distribution, |
| 57 | ! |
category_distribution = category_distribution |
| 58 |
)) |
|
| 59 |
} |
|
| 60 | ||
| 61 |
#' Validate function dependencies |
|
| 62 |
#' |
|
| 63 |
#' @param cran_functions Functions selected for CRAN |
|
| 64 |
#' @param function_analysis Function analysis results |
|
| 65 |
#' @return Dependency validation results |
|
| 66 |
validate_dependencies <- function(cran_functions, function_analysis) {
|
|
| 67 |
# Check for circular dependencies |
|
| 68 | ! |
circular_deps <- character(0) |
| 69 | ||
| 70 |
# Check for missing dependencies |
|
| 71 | ! |
missing_deps <- character(0) |
| 72 | ||
| 73 | ! |
for (func_name in cran_functions) {
|
| 74 | ! |
if (func_name %in% names(function_analysis)) {
|
| 75 | ! |
func_deps <- function_analysis[[func_name]]$dependencies |
| 76 | ||
| 77 |
# Check if dependencies are available |
|
| 78 | ! |
for (dep in func_deps) {
|
| 79 | ! |
if (!dep %in% cran_functions && !dep %in% c("base", "utils", "stats", "graphics")) {
|
| 80 | ! |
missing_deps <- c(missing_deps, paste(func_name, "->", dep)) |
| 81 |
} |
|
| 82 |
} |
|
| 83 |
} |
|
| 84 |
} |
|
| 85 | ||
| 86 | ! |
return(list( |
| 87 | ! |
has_circular_dependencies = length(circular_deps) > 0, |
| 88 | ! |
missing_dependencies = missing_deps, |
| 89 | ! |
dependency_count = length(missing_deps) |
| 90 |
)) |
|
| 91 |
} |
|
| 92 | ||
| 93 |
#' Validate function documentation |
|
| 94 |
#' |
|
| 95 |
#' @param cran_functions Functions selected for CRAN |
|
| 96 |
#' @param function_analysis Function analysis results |
|
| 97 |
#' @return Documentation validation results |
|
| 98 |
validate_documentation <- function(cran_functions, function_analysis) {
|
|
| 99 | ! |
documented_functions <- 0 |
| 100 | ! |
functions_with_examples <- 0 |
| 101 | ! |
functions_with_tests <- 0 |
| 102 | ||
| 103 | ! |
for (func_name in cran_functions) {
|
| 104 | ! |
if (func_name %in% names(function_analysis)) {
|
| 105 | ! |
func_info <- function_analysis[[func_name]] |
| 106 | ||
| 107 | ! |
if (func_info$documentation == "Complete") {
|
| 108 | ! |
documented_functions <- documented_functions + 1 |
| 109 |
} |
|
| 110 | ||
| 111 | ! |
if (func_info$usage$in_examples) {
|
| 112 | ! |
functions_with_examples <- functions_with_examples + 1 |
| 113 |
} |
|
| 114 | ||
| 115 | ! |
if (func_info$usage$in_tests) {
|
| 116 | ! |
functions_with_tests <- functions_with_tests + 1 |
| 117 |
} |
|
| 118 |
} |
|
| 119 |
} |
|
| 120 | ||
| 121 | ! |
total_functions <- length(cran_functions) |
| 122 | ||
| 123 | ! |
return(list( |
| 124 | ! |
total_functions = total_functions, |
| 125 | ! |
documented_functions = documented_functions, |
| 126 | ! |
documentation_coverage = if (total_functions > 0) documented_functions / total_functions else 0, |
| 127 | ! |
functions_with_examples = functions_with_examples, |
| 128 | ! |
example_coverage = if (total_functions > 0) functions_with_examples / total_functions else 0, |
| 129 | ! |
functions_with_tests = functions_with_tests, |
| 130 | ! |
test_coverage = if (total_functions > 0) functions_with_tests / total_functions else 0 |
| 131 |
)) |
|
| 132 |
} |
|
| 133 | ||
| 134 |
#' Validate test coverage |
|
| 135 |
#' |
|
| 136 |
#' @param cran_functions Functions selected for CRAN |
|
| 137 |
#' @param function_analysis Function analysis results |
|
| 138 |
#' @return Test coverage validation results |
|
| 139 |
validate_test_coverage <- function(cran_functions, function_analysis) {
|
|
| 140 |
# This would integrate with covr package for actual test coverage |
|
| 141 |
# For now, we'll use the usage analysis |
|
| 142 | ||
| 143 | ! |
tested_functions <- 0 |
| 144 | ! |
for (func_name in cran_functions) {
|
| 145 | ! |
if (func_name %in% names(function_analysis)) {
|
| 146 | ! |
if (function_analysis[[func_name]]$usage$in_tests) {
|
| 147 | ! |
tested_functions <- tested_functions + 1 |
| 148 |
} |
|
| 149 |
} |
|
| 150 |
} |
|
| 151 | ||
| 152 | ! |
total_functions <- length(cran_functions) |
| 153 | ||
| 154 | ! |
return(list( |
| 155 | ! |
total_functions = total_functions, |
| 156 | ! |
tested_functions = tested_functions, |
| 157 | ! |
test_coverage_percentage = if (total_functions > 0) round(100 * tested_functions / total_functions, 1) else 0, |
| 158 | ! |
meets_coverage_target = tested_functions / total_functions >= 0.9 |
| 159 |
)) |
|
| 160 |
} |
|
| 161 | ||
| 162 |
#' Validate CRAN compliance |
|
| 163 |
#' |
|
| 164 |
#' @param cran_functions Functions selected for CRAN |
|
| 165 |
#' @param function_analysis Function analysis results |
|
| 166 |
#' @return CRAN compliance validation results |
|
| 167 |
validate_cran_compliance <- function(cran_functions, function_analysis) {
|
|
| 168 |
# Check function count |
|
| 169 | ! |
function_count_ok <- length(cran_functions) <= 30 |
| 170 | ||
| 171 |
# Check documentation completeness |
|
| 172 | ! |
doc_results <- validate_documentation(cran_functions, function_analysis) |
| 173 | ! |
documentation_ok <- doc_results$documentation_coverage >= 0.95 |
| 174 | ||
| 175 |
# Check test coverage |
|
| 176 | ! |
test_results <- validate_test_coverage(cran_functions, function_analysis) |
| 177 | ! |
test_coverage_ok <- test_results$meets_coverage_target |
| 178 | ||
| 179 |
# Check for proper function names |
|
| 180 | ! |
function_names_ok <- all(grepl("^[a-zA-Z][a-zA-Z0-9_.]*$", cran_functions))
|
| 181 | ||
| 182 |
# Check for examples |
|
| 183 | ! |
examples_ok <- doc_results$example_coverage >= 0.8 |
| 184 | ||
| 185 | ! |
return(list( |
| 186 | ! |
function_count_ok = function_count_ok, |
| 187 | ! |
documentation_ok = documentation_ok, |
| 188 | ! |
test_coverage_ok = test_coverage_ok, |
| 189 | ! |
function_names_ok = function_names_ok, |
| 190 | ! |
examples_ok = examples_ok, |
| 191 | ! |
overall_compliance = function_count_ok && documentation_ok && test_coverage_ok && |
| 192 | ! |
function_names_ok && examples_ok |
| 193 |
)) |
|
| 194 |
} |
|
| 195 | ||
| 196 |
#' Print validation summary |
|
| 197 |
#' |
|
| 198 |
#' @param validation_results Validation results |
|
| 199 |
print_validation_summary <- function(validation_results) {
|
|
| 200 | ! |
cat("\nš VALIDATION SUMMARY\n")
|
| 201 | ! |
cat(paste(rep("=", 20), collapse = ""), "\n")
|
| 202 | ||
| 203 |
# Function count |
|
| 204 | ! |
cat(sprintf( |
| 205 | ! |
"Function Count: %d/30 %s\n", |
| 206 | ! |
validation_results$function_count, |
| 207 | ! |
if (validation_results$function_count <= 30) "ā " else "ā" |
| 208 |
)) |
|
| 209 | ||
| 210 |
# Category completeness |
|
| 211 | ! |
cat_complete <- validation_results$category_completeness$categories_complete |
| 212 | ! |
cat(sprintf("Categories Complete: %s\n", if (cat_complete) "ā
" else "ā"))
|
| 213 | ||
| 214 |
# Documentation |
|
| 215 | ! |
doc_coverage <- validation_results$documentation_check$documentation_coverage |
| 216 | ! |
cat(sprintf( |
| 217 | ! |
"Documentation Coverage: %.1f%% %s\n", |
| 218 | ! |
doc_coverage * 100, |
| 219 | ! |
if (doc_coverage >= 0.95) "ā " else "ā" |
| 220 |
)) |
|
| 221 | ||
| 222 |
# Test coverage |
|
| 223 | ! |
test_coverage <- validation_results$test_coverage$test_coverage_percentage |
| 224 | ! |
cat(sprintf( |
| 225 | ! |
"Test Coverage: %.1f%% %s\n", |
| 226 | ! |
test_coverage, |
| 227 | ! |
if (test_coverage >= 90) "ā " else "ā" |
| 228 |
)) |
|
| 229 | ||
| 230 |
# CRAN compliance |
|
| 231 | ! |
cran_compliance <- validation_results$cran_compliance$overall_compliance |
| 232 | ! |
cat(sprintf("CRAN Compliance: %s\n", if (cran_compliance) "ā
" else "ā"))
|
| 233 | ||
| 234 | ! |
cat("\n")
|
| 235 |
} |
|
| 236 | ||
| 237 |
#' Generate validation report |
|
| 238 |
#' |
|
| 239 |
#' @param validation_results Validation results |
|
| 240 |
#' @param function_categories Function categories |
|
| 241 |
#' @param cran_functions Functions selected for CRAN |
|
| 242 |
#' @return Validation report |
|
| 243 |
generate_validation_report <- function(validation_results, function_categories, cran_functions) {
|
|
| 244 | ! |
cat("š Generating validation report...\n")
|
| 245 | ||
| 246 | ! |
report <- list( |
| 247 | ! |
summary = list( |
| 248 | ! |
validation_timestamp = Sys.time(), |
| 249 | ! |
total_functions_audited = length(unlist(function_categories)), |
| 250 | ! |
cran_functions_selected = length(cran_functions), |
| 251 | ! |
validation_passed = validation_results$cran_compliance$overall_compliance |
| 252 |
), |
|
| 253 | ! |
validation_results = validation_results, |
| 254 | ! |
function_categories = function_categories, |
| 255 | ! |
cran_functions = cran_functions, |
| 256 | ! |
recommendations = generate_validation_recommendations(validation_results) |
| 257 |
) |
|
| 258 | ||
| 259 | ! |
cat("ā
Validation report generated\n")
|
| 260 | ||
| 261 | ! |
return(report) |
| 262 |
} |
|
| 263 | ||
| 264 |
#' Generate validation recommendations |
|
| 265 |
#' |
|
| 266 |
#' @param validation_results Validation results |
|
| 267 |
#' @return Validation recommendations |
|
| 268 |
generate_validation_recommendations <- function(validation_results) {
|
|
| 269 | ! |
recommendations <- character(0) |
| 270 | ||
| 271 |
# Function count recommendations |
|
| 272 | ! |
if (!validation_results$cran_compliance$function_count_ok) {
|
| 273 | ! |
recommendations <- c( |
| 274 | ! |
recommendations, |
| 275 | ! |
"Reduce function count to 30 or fewer for optimal CRAN submission" |
| 276 |
) |
|
| 277 |
} |
|
| 278 | ||
| 279 |
# Documentation recommendations |
|
| 280 | ! |
if (!validation_results$cran_compliance$documentation_ok) {
|
| 281 | ! |
recommendations <- c( |
| 282 | ! |
recommendations, |
| 283 | ! |
"Improve documentation coverage to 95% or higher" |
| 284 |
) |
|
| 285 |
} |
|
| 286 | ||
| 287 |
# Test coverage recommendations |
|
| 288 | ! |
if (!validation_results$cran_compliance$test_coverage_ok) {
|
| 289 | ! |
recommendations <- c( |
| 290 | ! |
recommendations, |
| 291 | ! |
"Increase test coverage to 90% or higher" |
| 292 |
) |
|
| 293 |
} |
|
| 294 | ||
| 295 |
# Examples recommendations |
|
| 296 | ! |
if (!validation_results$cran_compliance$examples_ok) {
|
| 297 | ! |
recommendations <- c( |
| 298 | ! |
recommendations, |
| 299 | ! |
"Add examples to 80% or more of functions" |
| 300 |
) |
|
| 301 |
} |
|
| 302 | ||
| 303 |
# Dependency recommendations |
|
| 304 | ! |
if (validation_results$dependency_check$dependency_count > 0) {
|
| 305 | ! |
recommendations <- c( |
| 306 | ! |
recommendations, |
| 307 | ! |
paste( |
| 308 | ! |
"Resolve", validation_results$dependency_check$dependency_count, |
| 309 | ! |
"missing dependencies" |
| 310 |
) |
|
| 311 |
) |
|
| 312 |
} |
|
| 313 | ||
| 314 | ! |
if (length(recommendations) == 0) {
|
| 315 | ! |
recommendations <- "All validation checks passed - ready for CRAN submission" |
| 316 |
} |
|
| 317 | ||
| 318 | ! |
return(recommendations) |
| 319 |
} |
|
| 320 | ||
| 321 |
#' Test validation system |
|
| 322 |
#' |
|
| 323 |
#' @return Test results |
|
| 324 |
test_validation_system <- function() {
|
|
| 325 | ! |
cat("š§Ŗ Testing validation system...\n")
|
| 326 | ||
| 327 |
# Test with sample data |
|
| 328 | ! |
sample_categories <- list( |
| 329 | ! |
core_workflow = c("analyze_transcripts", "load_zoom_transcript"),
|
| 330 | ! |
privacy_compliance = c("privacy_audit"),
|
| 331 | ! |
data_processing = c("consolidate_transcript"),
|
| 332 | ! |
analysis = c("summarize_transcript_metrics"),
|
| 333 | ! |
visualization = c("plot_users"),
|
| 334 | ! |
utility = c("get_essential_functions")
|
| 335 |
) |
|
| 336 | ||
| 337 | ! |
sample_cran_functions <- c( |
| 338 | ! |
"analyze_transcripts", "load_zoom_transcript", "privacy_audit", |
| 339 | ! |
"consolidate_transcript", "summarize_transcript_metrics", "plot_users" |
| 340 |
) |
|
| 341 | ||
| 342 | ! |
sample_analysis <- list( |
| 343 | ! |
analyze_transcripts = list( |
| 344 | ! |
documentation = "Complete", |
| 345 | ! |
usage = list(in_tests = TRUE, in_examples = TRUE), |
| 346 | ! |
dependencies = character(0) |
| 347 |
), |
|
| 348 | ! |
load_zoom_transcript = list( |
| 349 | ! |
documentation = "Complete", |
| 350 | ! |
usage = list(in_tests = TRUE, in_examples = TRUE), |
| 351 | ! |
dependencies = character(0) |
| 352 |
) |
|
| 353 |
) |
|
| 354 | ||
| 355 | ! |
validation_results <- validate_audit_results(sample_categories, sample_cran_functions, sample_analysis) |
| 356 | ||
| 357 | ! |
cat("ā
Validation system test completed\n")
|
| 358 | ||
| 359 | ! |
return(validation_results) |
| 360 |
} |
| 1 |
#' Detect Duplicate Transcripts |
|
| 2 |
#' |
|
| 3 |
#' Identifies and analyzes duplicate Zoom transcript files using multiple detection methods. |
|
| 4 |
#' This function helps clean up transcript datasets by finding files that contain similar |
|
| 5 |
#' or identical content, which can occur when multiple transcript formats are generated |
|
| 6 |
#' for the same recording session. |
|
| 7 |
#' |
|
| 8 |
#' @param transcript_list A tibble containing transcript file information with a |
|
| 9 |
#' `transcript_file` column containing file names |
|
| 10 |
#' @param data_folder Overall data folder for your recordings and data. Defaults to "data" |
|
| 11 |
#' @param transcripts_folder Specific subfolder of the data folder where transcript files |
|
| 12 |
#' are stored. Defaults to "transcripts" |
|
| 13 |
#' @param similarity_threshold Threshold for considering transcripts as duplicates (0-1). |
|
| 14 |
#' Higher values require more similarity. Defaults to 0.95 |
|
| 15 |
#' @param method Method for detecting duplicates. One of: |
|
| 16 |
#' - "hybrid" (default): Combines metadata and content analysis |
|
| 17 |
#' - "content": Analyzes actual transcript content |
|
| 18 |
#' - "metadata": Compares file metadata only |
|
| 19 |
#' @param names_to_exclude Character vector of names to exclude from content comparison. |
|
| 20 |
#' Defaults to c("dead_air") to ignore silence periods
|
|
| 21 |
#' |
|
| 22 |
#' @return A list containing duplicate detection results with the following elements: |
|
| 23 |
#' \describe{
|
|
| 24 |
#' \item{duplicate_groups}{List of groups containing duplicate file names}
|
|
| 25 |
#' \item{similarity_matrix}{Matrix of similarity scores between all file pairs}
|
|
| 26 |
#' \item{recommendations}{Character vector of recommendations for handling duplicates}
|
|
| 27 |
#' \item{summary}{List with summary statistics: total_files, duplicate_groups, total_duplicates}
|
|
| 28 |
#' } |
|
| 29 |
#' |
|
| 30 |
#' @export |
|
| 31 |
#' |
|
| 32 |
#' @examples |
|
| 33 |
#' # Create sample transcript list |
|
| 34 |
#' transcript_list <- tibble::tibble( |
|
| 35 |
#' transcript_file = c( |
|
| 36 |
#' "GMT20240115-100000_Recording.transcript.vtt", |
|
| 37 |
#' "GMT20240115-100000_Recording.cc.vtt", |
|
| 38 |
#' "GMT20240116-140000_Recording.transcript.vtt" |
|
| 39 |
#' ) |
|
| 40 |
#' ) |
|
| 41 |
#' |
|
| 42 |
#' # Detect duplicates in a transcript list |
|
| 43 |
#' duplicates <- detect_duplicate_transcripts(transcript_list) |
|
| 44 |
#' |
|
| 45 |
#' # View duplicate groups |
|
| 46 |
#' duplicates$duplicate_groups |
|
| 47 |
#' |
|
| 48 |
#' # View recommendations |
|
| 49 |
#' duplicates$recommendations |
|
| 50 |
#' |
|
| 51 |
#' # Use different detection method |
|
| 52 |
#' content_duplicates <- detect_duplicate_transcripts( |
|
| 53 |
#' transcript_list, |
|
| 54 |
#' method = "content", |
|
| 55 |
#' similarity_threshold = 0.9 |
|
| 56 |
#' ) |
|
| 57 |
#' |
|
| 58 |
detect_duplicate_transcripts <- function( |
|
| 59 |
transcript_list = NULL, |
|
| 60 |
data_folder = ".", |
|
| 61 |
transcripts_folder = "transcripts", |
|
| 62 |
similarity_threshold = 0.95, |
|
| 63 |
method = c("hybrid", "content", "metadata"),
|
|
| 64 |
names_to_exclude = c("dead_air")) {
|
|
| 65 | 35x |
method <- match.arg(method) |
| 66 | ||
| 67 |
# Validate similarity threshold |
|
| 68 | 34x |
if (similarity_threshold < 0 || similarity_threshold > 1) {
|
| 69 | 2x |
warning("similarity_threshold should be between 0 and 1, clamping to valid range")
|
| 70 | 2x |
similarity_threshold <- max(0, min(1, similarity_threshold)) |
| 71 |
} |
|
| 72 | ||
| 73 | 34x |
if (!tibble::is_tibble(transcript_list)) {
|
| 74 | 2x |
stop("transcript_list must be a tibble")
|
| 75 |
} |
|
| 76 | ||
| 77 | 32x |
if (nrow(transcript_list) == 0) {
|
| 78 | 17x |
return(list( |
| 79 | 17x |
duplicate_groups = list(), |
| 80 | 17x |
similarity_matrix = matrix(numeric(0), nrow = 0, ncol = 0), |
| 81 | 17x |
recommendations = character(0), |
| 82 | 17x |
summary = list( |
| 83 | 17x |
total_files = 0, |
| 84 | 17x |
duplicate_groups = 0, |
| 85 | 17x |
total_duplicates = 0 |
| 86 |
) |
|
| 87 |
)) |
|
| 88 |
} |
|
| 89 | ||
| 90 |
# Get transcript file names |
|
| 91 | 15x |
transcript_files <- transcript_list$transcript_file |
| 92 | 15x |
transcript_files <- transcript_files[!is.na(transcript_files)] |
| 93 | ||
| 94 | 15x |
if (length(transcript_files) == 0) {
|
| 95 | 1x |
return(list( |
| 96 | 1x |
duplicate_groups = list(), |
| 97 | 1x |
similarity_matrix = matrix(numeric(0), nrow = 0, ncol = 0), |
| 98 | 1x |
recommendations = character(0), |
| 99 | 1x |
summary = list( |
| 100 | 1x |
total_files = 0, |
| 101 | 1x |
duplicate_groups = 0, |
| 102 | 1x |
total_duplicates = 0 |
| 103 |
) |
|
| 104 |
)) |
|
| 105 |
} |
|
| 106 | ||
| 107 |
# Build full paths |
|
| 108 | 14x |
transcripts_folder_path <- paste0(data_folder, "/", transcripts_folder, "/") |
| 109 | 14x |
full_paths <- paste0(transcripts_folder_path, transcript_files) |
| 110 | ||
| 111 |
# Check which files exist |
|
| 112 | 14x |
existing_files <- full_paths[file.exists(full_paths)] |
| 113 | 14x |
existing_names <- basename(existing_files) |
| 114 | ||
| 115 | 14x |
if (length(existing_files) == 0) {
|
| 116 |
# Only show warnings if not in test environment |
|
| 117 | 4x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 118 | 1x |
warning("No transcript files found in the specified directory")
|
| 119 |
} |
|
| 120 | 4x |
return(list( |
| 121 | 4x |
duplicate_groups = list(), |
| 122 | 4x |
similarity_matrix = matrix(numeric(0), nrow = 0, ncol = 0), |
| 123 | 4x |
recommendations = character(0), |
| 124 | 4x |
summary = list( |
| 125 | 4x |
total_files = 0, |
| 126 | 4x |
duplicate_groups = 0, |
| 127 | 4x |
total_duplicates = 0 |
| 128 |
) |
|
| 129 |
)) |
|
| 130 |
} |
|
| 131 | ||
| 132 |
# Initialize results |
|
| 133 | 10x |
duplicate_groups <- list() |
| 134 | 10x |
similarity_matrix <- matrix(0, nrow = length(existing_names), ncol = length(existing_names)) |
| 135 | 10x |
rownames(similarity_matrix) <- existing_names |
| 136 | 10x |
colnames(similarity_matrix) <- existing_names |
| 137 | ||
| 138 |
# Detect duplicates based on method |
|
| 139 | 10x |
if (method %in% c("metadata", "hybrid")) {
|
| 140 |
# Get file metadata |
|
| 141 | 3x |
file_info <- file.info(existing_files) |
| 142 | 3x |
file_sizes <- file_info$size |
| 143 | 3x |
file_mtimes <- file_info$mtime |
| 144 | ||
| 145 |
# Compare file sizes and modification times |
|
| 146 | 3x |
for (i in seq_along(existing_names)) {
|
| 147 | 7x |
for (j in i:length(existing_names)) {
|
| 148 | 12x |
if (i == j) {
|
| 149 | 7x |
similarity_matrix[i, j] <- 1.0 |
| 150 |
} else {
|
|
| 151 |
# Size similarity |
|
| 152 | 5x |
size_sim <- 1 - abs(file_sizes[i] - file_sizes[j]) / max(file_sizes[i], file_sizes[j]) |
| 153 | ||
| 154 |
# Time similarity (within 1 hour = similar) |
|
| 155 | 5x |
time_diff <- abs(as.numeric(file_mtimes[i] - file_mtimes[j])) |
| 156 | 5x |
time_sim <- ifelse(time_diff < 3600, 1.0, max(0, 1 - time_diff / 86400)) # 1 day max |
| 157 | ||
| 158 |
# Combined metadata similarity |
|
| 159 | 5x |
metadata_sim <- (size_sim + time_sim) / 2 |
| 160 | 5x |
similarity_matrix[i, j] <- metadata_sim |
| 161 | 5x |
similarity_matrix[j, i] <- metadata_sim |
| 162 |
} |
|
| 163 |
} |
|
| 164 |
} |
|
| 165 |
} |
|
| 166 | ||
| 167 | 10x |
if (method %in% c("content", "hybrid")) {
|
| 168 |
# Load and compare transcript content |
|
| 169 | 8x |
transcript_data <- list() |
| 170 | ||
| 171 |
# Load all transcripts |
|
| 172 | 8x |
for (i in seq_along(existing_files)) {
|
| 173 | 18x |
tryCatch( |
| 174 |
{
|
|
| 175 | 18x |
transcript_data[[i]] <- load_zoom_transcript(existing_files[i]) |
| 176 |
}, |
|
| 177 | 18x |
error = function(e) {
|
| 178 | 1x |
warning(paste("Could not load transcript:", existing_names[i], "-", e$message))
|
| 179 | 1x |
transcript_data[[i]] <- NULL |
| 180 |
} |
|
| 181 |
) |
|
| 182 |
} |
|
| 183 | ||
| 184 |
# Compare content |
|
| 185 | 8x |
for (i in seq_along(existing_names)) {
|
| 186 | 18x |
for (j in i:length(existing_names)) {
|
| 187 | 30x |
if (i == j) {
|
| 188 | 18x |
similarity_matrix[i, j] <- 1.0 |
| 189 |
} else {
|
|
| 190 | 12x |
content_sim <- calculate_content_similarity( |
| 191 | 12x |
transcript_data[[i]], |
| 192 | 12x |
transcript_data[[j]], |
| 193 | 12x |
names_to_exclude |
| 194 |
) |
|
| 195 | ||
| 196 | 12x |
if (method == "hybrid") {
|
| 197 |
# Combine metadata and content similarity |
|
| 198 | 1x |
similarity_matrix[i, j] <- (similarity_matrix[i, j] + content_sim) / 2 |
| 199 | 1x |
similarity_matrix[j, i] <- similarity_matrix[i, j] |
| 200 |
} else {
|
|
| 201 | 11x |
similarity_matrix[i, j] <- content_sim |
| 202 | 11x |
similarity_matrix[j, i] <- content_sim |
| 203 |
} |
|
| 204 |
} |
|
| 205 |
} |
|
| 206 |
} |
|
| 207 |
} |
|
| 208 | ||
| 209 |
# Find duplicate groups |
|
| 210 | 10x |
processed <- logical(length(existing_names)) |
| 211 | ||
| 212 | 10x |
for (i in seq_along(existing_names)) {
|
| 213 | 23x |
if (!processed[i]) {
|
| 214 |
# Find all files similar to this one |
|
| 215 | 12x |
similar_files <- which(similarity_matrix[i, ] >= similarity_threshold) |
| 216 | ||
| 217 | 12x |
if (length(similar_files) > 1) {
|
| 218 | 8x |
duplicate_groups[[length(duplicate_groups) + 1]] <- existing_names[similar_files] |
| 219 | 8x |
processed[similar_files] <- TRUE |
| 220 |
} |
|
| 221 |
} |
|
| 222 |
} |
|
| 223 | ||
| 224 |
# Generate recommendations |
|
| 225 | 10x |
recommendations <- character(length(duplicate_groups)) |
| 226 | 10x |
for (i in seq_along(duplicate_groups)) {
|
| 227 | 8x |
group <- duplicate_groups[[i]] |
| 228 | 8x |
if (length(group) == 2) {
|
| 229 | 5x |
recommendations[i] <- paste("Keep", group[1], "and remove", group[2])
|
| 230 |
} else {
|
|
| 231 | 3x |
recommendations[i] <- paste("Keep", group[1], "and remove", paste(group[-1], collapse = ", "))
|
| 232 |
} |
|
| 233 |
} |
|
| 234 | ||
| 235 |
# Create summary |
|
| 236 | 10x |
total_duplicates <- if (length(duplicate_groups) > 0) {
|
| 237 | 8x |
sum(sapply(duplicate_groups, length) - 1) |
| 238 |
} else {
|
|
| 239 | 2x |
0 |
| 240 |
} |
|
| 241 | ||
| 242 | 10x |
summary <- list( |
| 243 | 10x |
total_files = length(existing_names), |
| 244 | 10x |
duplicate_groups = length(duplicate_groups), |
| 245 | 10x |
total_duplicates = total_duplicates, |
| 246 | 10x |
similarity_threshold = similarity_threshold, |
| 247 | 10x |
method = method |
| 248 |
) |
|
| 249 | ||
| 250 | 10x |
return(list( |
| 251 | 10x |
duplicate_groups = duplicate_groups, |
| 252 | 10x |
similarity_matrix = similarity_matrix, |
| 253 | 10x |
recommendations = recommendations, |
| 254 | 10x |
summary = summary |
| 255 |
)) |
|
| 256 |
} |
| 1 |
#' FERPA Compliance Functions |
|
| 2 |
#' |
|
| 3 |
#' Functions to validate and ensure FERPA compliance for educational data. |
|
| 4 |
#' These functions help institutions maintain compliance with the Family |
|
| 5 |
#' Educational Rights and Privacy Act (FERPA) when using this package. |
|
| 6 |
#' |
|
| 7 |
#' @name ferpa_compliance |
|
| 8 |
#' @keywords internal |
|
| 9 |
NULL |
|
| 10 | ||
| 11 |
#' Validate FERPA Compliance |
|
| 12 |
#' |
|
| 13 |
#' Validates data for FERPA compliance by checking for personally identifiable |
|
| 14 |
#' information (PII) and validating data handling procedures. |
|
| 15 |
#' |
|
| 16 |
#' **CRITICAL ETHICAL COMPLIANCE**: This function is essential for ensuring |
|
| 17 |
#' educational data protection and FERPA compliance. It helps institutions |
|
| 18 |
#' maintain legal compliance while using student data for educational improvement. |
|
| 19 |
#' |
|
| 20 |
#' @param data A data frame or tibble to validate |
|
| 21 |
#' @param institution_type Type of institution. One of `c("educational", "research", "mixed")`
|
|
| 22 |
#' @param check_retention Whether to check data retention policies |
|
| 23 |
#' @param retention_period Retention period to check against. One of `c("academic_year", "semester", "quarter", "custom")`
|
|
| 24 |
#' @param custom_retention_days Custom retention period in days (used when retention_period = "custom") |
|
| 25 |
#' @param audit_log Whether to log compliance checks for institutional review |
|
| 26 |
#' |
|
| 27 |
#' @return A list containing compliance validation results with the following elements: |
|
| 28 |
#' - `compliant`: Logical indicating overall compliance |
|
| 29 |
#' - `pii_detected`: Character vector of detected PII fields |
|
| 30 |
#' - `recommendations`: Character vector of compliance recommendations |
|
| 31 |
#' - `retention_check`: Data retention validation results (if requested) |
|
| 32 |
#' - `institution_guidance`: Institution-specific recommendations |
|
| 33 |
#' |
|
| 34 |
#' @export |
|
| 35 |
#' |
|
| 36 |
#' @examples |
|
| 37 |
#' # Validate sample data for FERPA compliance |
|
| 38 |
#' sample_data <- tibble::tibble( |
|
| 39 |
#' student_id = c("12345", "67890"),
|
|
| 40 |
#' preferred_name = c("Alice Johnson", "Bob Smith"),
|
|
| 41 |
#' email = c("alice@university.edu", "bob@university.edu"),
|
|
| 42 |
#' participation_score = c(85, 92) |
|
| 43 |
#' ) |
|
| 44 |
#' |
|
| 45 |
#' validation_result <- validate_ferpa_compliance(sample_data) |
|
| 46 |
#' print(validation_result$compliant) |
|
| 47 |
#' print(validation_result$recommendations) |
|
| 48 |
validate_ferpa_compliance <- function(data = NULL, |
|
| 49 |
institution_type = c("educational", "research", "mixed"),
|
|
| 50 |
check_retention = TRUE, |
|
| 51 |
retention_period = c("academic_year", "semester", "quarter", "custom"),
|
|
| 52 |
custom_retention_days = NULL, |
|
| 53 |
audit_log = TRUE) {
|
|
| 54 | 15x |
institution_type <- match.arg(institution_type) |
| 55 | 15x |
retention_period <- match.arg(retention_period) |
| 56 | ||
| 57 | 15x |
if (!is.data.frame(data)) {
|
| 58 | ! |
abort_zse("`data` must be a data.frame or tibble", class = "zse_input_error")
|
| 59 |
} |
|
| 60 | ||
| 61 |
# Initialize results |
|
| 62 | 15x |
result <- list( |
| 63 | 15x |
compliant = TRUE, |
| 64 | 15x |
pii_detected = character(0), |
| 65 | 15x |
recommendations = character(0), |
| 66 | 15x |
retention_check = NULL, |
| 67 | 15x |
institution_guidance = character(0) |
| 68 |
) |
|
| 69 | ||
| 70 |
# Check for PII fields |
|
| 71 | 15x |
pii_patterns <- c( |
| 72 | 15x |
"student_id", "studentid", "student_id_", "id", |
| 73 | 15x |
"preferred_name", "name", "first_last", "name_raw", |
| 74 | 15x |
"email", "email_address", "e_mail", |
| 75 | 15x |
"phone", "phone_number", "telephone", |
| 76 | 15x |
"address", "street_address", "home_address", |
| 77 | 15x |
"ssn", "social_security", "social_security_number", |
| 78 | 15x |
"birth_date", "birthday", "date_of_birth", |
| 79 | 15x |
"parent_name", "guardian_name" |
| 80 |
) |
|
| 81 | ||
| 82 | 15x |
detected_pii <- character(0) |
| 83 | 15x |
for (pattern in pii_patterns) {
|
| 84 | 375x |
matching_cols <- grep(pattern, names(data), ignore.case = TRUE, value = TRUE) |
| 85 | 375x |
detected_pii <- c(detected_pii, matching_cols) |
| 86 |
} |
|
| 87 | ||
| 88 | 15x |
result$pii_detected <- unique(detected_pii) |
| 89 | ||
| 90 |
# Generate recommendations based on PII detection |
|
| 91 | 15x |
if (length(result$pii_detected) > 0) {
|
| 92 | 13x |
result$compliant <- FALSE |
| 93 | 13x |
result$recommendations <- c( |
| 94 | 13x |
result$recommendations, |
| 95 | 13x |
paste("PII detected in columns:", paste(result$pii_detected, collapse = ", ")),
|
| 96 | 13x |
"Consider using ensure_privacy() to mask identifiable data", |
| 97 | 13x |
"Review institutional FERPA policies for data handling", |
| 98 | 13x |
"Ensure data access is limited to authorized personnel" |
| 99 |
) |
|
| 100 |
} |
|
| 101 | ||
| 102 |
# Log FERPA compliance check for audit purposes |
|
| 103 | 15x |
if (audit_log) {
|
| 104 | 15x |
log_ferpa_compliance_check( |
| 105 | 15x |
compliant = result$compliant, |
| 106 | 15x |
pii_detected = length(result$pii_detected), |
| 107 | 15x |
institution_type = institution_type, |
| 108 | 15x |
timestamp = Sys.time() |
| 109 |
) |
|
| 110 |
} |
|
| 111 | ||
| 112 |
# Institution-specific guidance |
|
| 113 | 15x |
if (institution_type == "educational") {
|
| 114 | 12x |
result$institution_guidance <- c( |
| 115 | 12x |
result$institution_guidance, |
| 116 | 12x |
"Educational institutions must comply with FERPA regulations", |
| 117 | 12x |
"Student records must be protected from unauthorized access", |
| 118 | 12x |
"Consider implementing role-based access controls", |
| 119 | 12x |
"Document all data access and usage procedures" |
| 120 |
) |
|
| 121 | 3x |
} else if (institution_type == "research") {
|
| 122 | 2x |
result$institution_guidance <- c( |
| 123 | 2x |
result$institution_guidance, |
| 124 | 2x |
"Research institutions should follow IRB guidelines", |
| 125 | 2x |
"Ensure proper consent procedures are in place", |
| 126 | 2x |
"Consider data anonymization for research publications", |
| 127 | 2x |
"Review institutional review board requirements" |
| 128 |
) |
|
| 129 | 1x |
} else if (institution_type == "mixed") {
|
| 130 | 1x |
result$institution_guidance <- c( |
| 131 | 1x |
result$institution_guidance, |
| 132 | 1x |
"Mixed institutions must comply with both FERPA and research ethics", |
| 133 | 1x |
"Implement separate procedures for educational vs. research data", |
| 134 | 1x |
"Ensure clear data classification and handling procedures", |
| 135 | 1x |
"Review both FERPA and IRB requirements" |
| 136 |
) |
|
| 137 |
} |
|
| 138 | ||
| 139 |
# Data retention check |
|
| 140 | 15x |
if (check_retention) {
|
| 141 | 14x |
result$retention_check <- check_data_retention_policy( |
| 142 | 14x |
data, |
| 143 | 14x |
retention_period = retention_period, |
| 144 | 14x |
custom_retention_days = custom_retention_days |
| 145 |
) |
|
| 146 | ||
| 147 | 14x |
if (!result$retention_check$compliant) {
|
| 148 | ! |
result$compliant <- FALSE |
| 149 | ! |
result$recommendations <- c( |
| 150 | ! |
result$recommendations, |
| 151 | ! |
result$retention_check$recommendations |
| 152 |
) |
|
| 153 |
} |
|
| 154 |
} |
|
| 155 | ||
| 156 |
# Additional compliance recommendations |
|
| 157 | 15x |
result$recommendations <- c( |
| 158 | 15x |
result$recommendations, |
| 159 | 15x |
"Use set_privacy_defaults('mask') for privacy-safe outputs",
|
| 160 | 15x |
"Implement secure data storage and transmission", |
| 161 | 15x |
"Train personnel on FERPA compliance requirements", |
| 162 | 15x |
"Maintain audit trails for data access and modifications" |
| 163 |
) |
|
| 164 | ||
| 165 | 15x |
result |
| 166 |
} |
|
| 167 | ||
| 168 |
#' Anonymize Educational Data |
|
| 169 |
#' |
|
| 170 |
#' Advanced anonymization for educational data that preserves data utility |
|
| 171 |
#' while ensuring FERPA compliance. |
|
| 172 |
#' |
|
| 173 |
#' @param data A data frame or tibble to anonymize |
|
| 174 |
#' @param method Anonymization method. One of `c("mask", "hash", "pseudonymize", "aggregate")`
|
|
| 175 |
#' @param preserve_columns Character vector of column names to preserve unchanged |
|
| 176 |
#' @param hash_salt Salt for hash-based anonymization (optional) |
|
| 177 |
#' @param aggregation_level Level for aggregation. One of `c("individual", "section", "course", "institution")`
|
|
| 178 |
#' |
|
| 179 |
#' @return The anonymized data frame with the same structure as input |
|
| 180 |
#' |
|
| 181 |
#' @export |
|
| 182 |
#' |
|
| 183 |
#' @examples |
|
| 184 |
#' # Anonymize sample data |
|
| 185 |
#' sample_data <- tibble::tibble( |
|
| 186 |
#' student_id = c("12345", "67890"),
|
|
| 187 |
#' preferred_name = c("Alice Johnson", "Bob Smith"),
|
|
| 188 |
#' section = c("A", "B"),
|
|
| 189 |
#' participation_score = c(85, 92) |
|
| 190 |
#' ) |
|
| 191 |
#' |
|
| 192 |
#' # Mask method (default) |
|
| 193 |
#' anonymized <- anonymize_educational_data(sample_data, method = "mask") |
|
| 194 |
#' |
|
| 195 |
#' # Hash method with salt |
|
| 196 |
#' hashed <- anonymize_educational_data(sample_data, method = "hash", hash_salt = "my_salt") |
|
| 197 |
anonymize_educational_data <- function(data = NULL, |
|
| 198 |
method = c("mask", "hash", "pseudonymize", "aggregate"),
|
|
| 199 |
preserve_columns = NULL, |
|
| 200 |
hash_salt = NULL, |
|
| 201 |
aggregation_level = c("individual", "section", "course", "institution")) {
|
|
| 202 | 12x |
method <- match.arg(method) |
| 203 | 12x |
aggregation_level <- match.arg(aggregation_level) |
| 204 | ||
| 205 | 12x |
if (!is.data.frame(data)) {
|
| 206 | 1x |
stop("Data must be a data frame or tibble", call. = FALSE)
|
| 207 |
} |
|
| 208 | ||
| 209 |
# Define PII columns to anonymize |
|
| 210 | 11x |
pii_columns <- c( |
| 211 | 11x |
"student_id", "studentid", "student_id_", |
| 212 | 11x |
"preferred_name", "name", "first_last", "name_raw", |
| 213 | 11x |
"email", "email_address", "e_mail", |
| 214 | 11x |
"phone", "phone_number", "telephone" |
| 215 |
) |
|
| 216 | ||
| 217 |
# Find columns to anonymize |
|
| 218 | 11x |
columns_to_anonymize <- intersect(pii_columns, names(data)) |
| 219 | 11x |
columns_to_preserve <- intersect(preserve_columns, names(data)) |
| 220 | 11x |
columns_to_anonymize <- setdiff(columns_to_anonymize, columns_to_preserve) |
| 221 | ||
| 222 | 11x |
if (length(columns_to_anonymize) == 0) {
|
| 223 | 1x |
diag_message("No PII columns found to anonymize")
|
| 224 | 1x |
return(data) |
| 225 |
} |
|
| 226 | ||
| 227 | 10x |
result <- data |
| 228 | ||
| 229 | 10x |
if (method == "mask") {
|
| 230 |
# Use existing ensure_privacy function |
|
| 231 | 4x |
result <- ensure_privacy(data, privacy_level = "mask") |
| 232 | 6x |
} else if (method == "hash") {
|
| 233 |
# Hash-based anonymization |
|
| 234 | 3x |
for (col in columns_to_anonymize) {
|
| 235 | 6x |
if (is.character(result[[col]]) || is.factor(result[[col]])) {
|
| 236 | 6x |
values <- as.character(result[[col]]) |
| 237 |
# Create deterministic hash |
|
| 238 | 6x |
hash_input <- if (!is.null(hash_salt)) paste0(values, hash_salt) else values |
| 239 | 6x |
hashed_values <- sapply(hash_input, function(x) {
|
| 240 | 14x |
if (is.na(x) || nchar(x) == 0) {
|
| 241 | ! |
return(x) |
| 242 |
} |
|
| 243 | 14x |
digest::digest(x, algo = "sha256", serialize = FALSE) |
| 244 |
}) |
|
| 245 | 6x |
result[[col]] <- substr(hashed_values, 1, 8) # Use first 8 characters |
| 246 |
} |
|
| 247 |
} |
|
| 248 | 3x |
} else if (method == "pseudonymize") {
|
| 249 |
# Pseudonymization with consistent mapping |
|
| 250 | 2x |
for (col in columns_to_anonymize) {
|
| 251 | 4x |
if (is.character(result[[col]]) || is.factor(result[[col]])) {
|
| 252 | 4x |
values <- as.character(result[[col]]) |
| 253 | 4x |
unique_vals <- unique(values[!is.na(values) & nchar(values) > 0]) |
| 254 | 4x |
if (length(unique_vals) > 0) {
|
| 255 |
# Create pseudonyms |
|
| 256 | 4x |
pseudonyms <- paste0("PSEUDO_", stringr::str_pad(seq_along(unique_vals), width = 3, pad = "0"))
|
| 257 | 4x |
mapping <- stats::setNames(pseudonyms, unique_vals) |
| 258 | 4x |
result[[col]] <- mapping[values] |
| 259 |
} |
|
| 260 |
} |
|
| 261 |
} |
|
| 262 | 1x |
} else if (method == "aggregate") {
|
| 263 |
# Aggregation-based anonymization |
|
| 264 | 1x |
if (aggregation_level == "individual") {
|
| 265 |
# Individual level - apply masking |
|
| 266 | 1x |
result <- ensure_privacy(data, privacy_level = "mask") |
| 267 |
} else {
|
|
| 268 |
# Higher aggregation levels |
|
| 269 | ! |
group_cols <- switch(aggregation_level, |
| 270 | ! |
"section" = intersect(c("section", "section_name"), names(data)),
|
| 271 | ! |
"course" = intersect(c("course", "course_id", "course_name"), names(data)),
|
| 272 | ! |
"institution" = character(0) |
| 273 |
) |
|
| 274 | ||
| 275 | ! |
if (length(group_cols) > 0) {
|
| 276 |
# Aggregate by group columns |
|
| 277 | ! |
result <- data %>% |
| 278 | ! |
dplyr::group_by(!!!rlang::syms(group_cols)) %>% |
| 279 | ! |
dplyr::summarise( |
| 280 | ! |
dplyr::across( |
| 281 | ! |
dplyr::where(is.numeric), |
| 282 | ! |
list( |
| 283 | ! |
mean = ~ mean(.x, na.rm = TRUE), |
| 284 | ! |
count = ~ dplyr::n(), |
| 285 | ! |
min = ~ min(.x, na.rm = TRUE), |
| 286 | ! |
max = ~ max(.x, na.rm = TRUE) |
| 287 |
), |
|
| 288 | ! |
.groups = "drop" |
| 289 |
) |
|
| 290 |
) |
|
| 291 |
} else {
|
|
| 292 |
# No group columns available, fall back to masking |
|
| 293 | ! |
result <- ensure_privacy(data, privacy_level = "mask") |
| 294 |
} |
|
| 295 |
} |
|
| 296 |
} |
|
| 297 | ||
| 298 | 10x |
result |
| 299 |
} |
|
| 300 | ||
| 301 |
#' Generate FERPA Compliance Report |
|
| 302 |
#' |
|
| 303 |
#' Generates comprehensive FERPA compliance reports for educational data. |
|
| 304 |
#' |
|
| 305 |
#' @param data A data frame or tibble to analyze |
|
| 306 |
#' @param output_file Optional file path to save the report |
|
| 307 |
#' @param report_format Report format. One of `c("text", "html", "json")`
|
|
| 308 |
#' @param include_audit_trail Whether to include audit trail information |
|
| 309 |
#' @param institution_info Optional list with institution information |
|
| 310 |
#' |
|
| 311 |
#' @return A list containing the compliance report |
|
| 312 |
#' |
|
| 313 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 314 |
#' |
|
| 315 |
#' @examples |
|
| 316 |
#' # Generate compliance report |
|
| 317 |
#' sample_data <- tibble::tibble( |
|
| 318 |
#' student_id = c("12345", "67890"),
|
|
| 319 |
#' preferred_name = c("Alice Johnson", "Bob Smith"),
|
|
| 320 |
#' participation_score = c(85, 92) |
|
| 321 |
#' ) |
|
| 322 |
#' |
|
| 323 |
#' report <- generate_ferpa_report(sample_data) |
|
| 324 |
#' print(report$summary) |
|
| 325 |
#' @export |
|
| 326 |
#' @keywords deprecated |
|
| 327 |
generate_ferpa_report <- function(data = NULL, |
|
| 328 |
output_file = NULL, |
|
| 329 |
report_format = c("text", "html", "json"),
|
|
| 330 |
include_audit_trail = TRUE, |
|
| 331 |
institution_info = NULL) {
|
|
| 332 |
# DEPRECATED: This function will be removed in the next version |
|
| 333 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 334 | 8x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 335 | ! |
warning("Function 'generate_ferpa_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 336 |
} |
|
| 337 | ||
| 338 | 8x |
report_format <- match.arg(report_format) |
| 339 | ||
| 340 |
# Validate data |
|
| 341 | 7x |
validation_result <- validate_ferpa_compliance(data) |
| 342 | ||
| 343 |
# Generate audit trail |
|
| 344 | 7x |
audit_trail <- if (include_audit_trail) {
|
| 345 | 6x |
list( |
| 346 | 6x |
report_generated = Sys.time(), |
| 347 | 6x |
data_rows = nrow(data), |
| 348 | 6x |
data_columns = ncol(data), |
| 349 | 6x |
pii_columns_detected = length(validation_result$pii_detected), |
| 350 | 6x |
privacy_level = getOption("zoomstudentengagement.privacy_level", "mask")
|
| 351 |
) |
|
| 352 |
} else {
|
|
| 353 | 1x |
NULL |
| 354 |
} |
|
| 355 | ||
| 356 |
# Build report |
|
| 357 | 7x |
report <- list( |
| 358 | 7x |
title = "FERPA Compliance Report", |
| 359 | 7x |
generated = Sys.time(), |
| 360 | 7x |
summary = list( |
| 361 | 7x |
compliant = validation_result$compliant, |
| 362 | 7x |
pii_detected = validation_result$pii_detected, |
| 363 | 7x |
recommendations_count = length(validation_result$recommendations) |
| 364 |
), |
|
| 365 | 7x |
validation_results = validation_result, |
| 366 | 7x |
audit_trail = audit_trail, |
| 367 | 7x |
institution_info = institution_info, |
| 368 | 7x |
recommendations = validation_result$recommendations |
| 369 |
) |
|
| 370 | ||
| 371 |
# Save to file if requested |
|
| 372 | 7x |
if (!is.null(output_file)) {
|
| 373 | 3x |
if (report_format == "json") {
|
| 374 | 1x |
jsonlite::write_json(report, output_file, pretty = TRUE) |
| 375 | 2x |
} else if (report_format == "html") {
|
| 376 |
# Create simple HTML report |
|
| 377 | 1x |
html_content <- paste0( |
| 378 | 1x |
"<html><head><title>FERPA Compliance Report</title></head><body>", |
| 379 | 1x |
"<h1>FERPA Compliance Report</h1>", |
| 380 | 1x |
"<p><strong>Generated:</strong> ", report$generated, "</p>", |
| 381 | 1x |
"<p><strong>Compliant:</strong> ", ifelse(report$summary$compliant, "Yes", "No"), "</p>", |
| 382 | 1x |
"<h2>Recommendations</h2><ul>", |
| 383 | 1x |
paste0("<li>", report$recommendations, "</li>", collapse = ""),
|
| 384 | 1x |
"</ul></body></html>" |
| 385 |
) |
|
| 386 | 1x |
writeLines(html_content, output_file) |
| 387 |
} else {
|
|
| 388 |
# Text format |
|
| 389 | 1x |
text_content <- paste0( |
| 390 | 1x |
"FERPA Compliance Report\n", |
| 391 | 1x |
"Generated: ", report$generated, "\n", |
| 392 | 1x |
"Compliant: ", ifelse(report$summary$compliant, "Yes", "No"), "\n", |
| 393 | 1x |
"PII Detected: ", paste(report$summary$pii_detected, collapse = ", "), "\n", |
| 394 | 1x |
"\nRecommendations:\n", |
| 395 | 1x |
paste0("- ", report$recommendations, collapse = "\n")
|
| 396 |
) |
|
| 397 | 1x |
writeLines(text_content, output_file) |
| 398 |
} |
|
| 399 |
} |
|
| 400 | ||
| 401 | 7x |
report |
| 402 |
} |
|
| 403 | ||
| 404 |
#' Check Data Retention Policy |
|
| 405 |
#' |
|
| 406 |
#' Validates data retention policies and identifies data that should be |
|
| 407 |
#' disposed of according to institutional policies. |
|
| 408 |
#' |
|
| 409 |
#' @param data A data frame or tibble to check |
|
| 410 |
#' @param retention_period Retention period to check against. One of `c("academic_year", "semester", "quarter", "custom")`
|
|
| 411 |
#' @param custom_retention_days Custom retention period in days (used when retention_period = "custom") |
|
| 412 |
#' @param date_column Column name containing dates to check against |
|
| 413 |
#' @param current_date Current date for comparison (defaults to Sys.Date()) |
|
| 414 |
#' |
|
| 415 |
#' @return A list containing retention validation results |
|
| 416 |
#' |
|
| 417 |
#' @export |
|
| 418 |
#' @keywords deprecated |
|
| 419 |
#' |
|
| 420 |
#' @examples |
|
| 421 |
#' # Check data retention policy |
|
| 422 |
#' sample_data <- tibble::tibble( |
|
| 423 |
#' student_id = c("12345", "67890"),
|
|
| 424 |
#' session_date = as.Date(c("2024-01-15", "2024-02-20")),
|
|
| 425 |
#' participation_score = c(85, 92) |
|
| 426 |
#' ) |
|
| 427 |
#' |
|
| 428 |
#' retention_check <- check_data_retention_policy( |
|
| 429 |
#' sample_data, |
|
| 430 |
#' retention_period = "academic_year", |
|
| 431 |
#' date_column = "session_date" |
|
| 432 |
#' ) |
|
| 433 |
#' print(retention_check$compliant) |
|
| 434 |
check_data_retention_policy <- function(data = NULL, |
|
| 435 |
retention_period = c("academic_year", "semester", "quarter", "custom"),
|
|
| 436 |
custom_retention_days = NULL, |
|
| 437 |
date_column = NULL, |
|
| 438 |
current_date = Sys.Date()) {
|
|
| 439 |
# DEPRECATED: This function will be removed in the next version |
|
| 440 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 441 | 23x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 442 | ! |
warning("Function 'check_data_retention_policy' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 443 |
} |
|
| 444 | ||
| 445 | 23x |
retention_period <- match.arg(retention_period) |
| 446 | ||
| 447 | 22x |
result <- list( |
| 448 | 22x |
compliant = TRUE, |
| 449 | 22x |
retention_period_days = 0, |
| 450 | 22x |
data_to_dispose = NULL, |
| 451 | 22x |
recommendations = character(0) |
| 452 |
) |
|
| 453 | ||
| 454 |
# Calculate retention period in days |
|
| 455 | 22x |
retention_days <- switch(retention_period, |
| 456 | 22x |
"academic_year" = 365, |
| 457 | 22x |
"semester" = 180, |
| 458 | 22x |
"quarter" = 90, |
| 459 | 22x |
"custom" = if (!is.null(custom_retention_days)) custom_retention_days else 365 |
| 460 |
) |
|
| 461 | ||
| 462 | 22x |
result$retention_period_days <- retention_days |
| 463 | ||
| 464 |
# Check date column if provided |
|
| 465 | 22x |
if (!is.null(date_column) && date_column %in% names(data)) {
|
| 466 | 7x |
if (is.character(data[[date_column]])) {
|
| 467 | ! |
dates <- as.Date(data[[date_column]]) |
| 468 | 7x |
} else if (inherits(data[[date_column]], "Date")) {
|
| 469 | 7x |
dates <- data[[date_column]] |
| 470 |
} else {
|
|
| 471 | ! |
dates <- as.Date(data[[date_column]]) |
| 472 |
} |
|
| 473 | ||
| 474 |
# Find data older than retention period |
|
| 475 | 7x |
cutoff_date <- current_date - retention_days |
| 476 | 7x |
old_data_indices <- which(dates < cutoff_date) |
| 477 | ||
| 478 | 7x |
if (length(old_data_indices) > 0) {
|
| 479 | 7x |
result$compliant <- FALSE |
| 480 | 7x |
result$data_to_dispose <- data[old_data_indices, ] |
| 481 | 7x |
result$recommendations <- c( |
| 482 | 7x |
result$recommendations, |
| 483 | 7x |
paste("Found", length(old_data_indices), "records older than retention period"),
|
| 484 | 7x |
paste("Cutoff date:", cutoff_date),
|
| 485 | 7x |
"Consider disposing of old data according to institutional policy", |
| 486 | 7x |
"Review data retention requirements with institutional compliance officer" |
| 487 |
) |
|
| 488 |
} |
|
| 489 |
} |
|
| 490 | ||
| 491 |
# General retention recommendations |
|
| 492 | 22x |
result$recommendations <- c( |
| 493 | 22x |
result$recommendations, |
| 494 | 22x |
paste("Retention period:", retention_period, "(", retention_days, "days)"),
|
| 495 | 22x |
"Implement automated data disposal procedures", |
| 496 | 22x |
"Document data retention and disposal procedures", |
| 497 | 22x |
"Train personnel on data retention requirements" |
| 498 |
) |
|
| 499 | ||
| 500 | 22x |
result |
| 501 |
} |
|
| 502 | ||
| 503 |
#' Log FERPA Compliance Check |
|
| 504 |
#' |
|
| 505 |
#' Internal function to log FERPA compliance checks for audit and institutional |
|
| 506 |
#' review purposes. |
|
| 507 |
#' |
|
| 508 |
#' @param compliant Whether the data is FERPA compliant |
|
| 509 |
#' @param pii_detected Number of PII fields detected |
|
| 510 |
#' @param institution_type Type of institution |
|
| 511 |
#' @param timestamp When the check was performed |
|
| 512 |
#' |
|
| 513 |
#' @keywords internal |
|
| 514 |
log_ferpa_compliance_check <- function(compliant, |
|
| 515 |
pii_detected, |
|
| 516 |
institution_type, |
|
| 517 |
timestamp = Sys.time()) {
|
|
| 518 |
# DEPRECATED: This function will be removed in the next version |
|
| 519 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 520 | 15x |
warning("Function 'log_ferpa_compliance_check' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 521 | ||
| 522 |
# Create log entry |
|
| 523 | 15x |
log_entry <- list( |
| 524 | 15x |
timestamp = timestamp, |
| 525 | 15x |
compliant = compliant, |
| 526 | 15x |
pii_detected = pii_detected, |
| 527 | 15x |
institution_type = institution_type, |
| 528 | 15x |
session_id = Sys.getpid() |
| 529 |
) |
|
| 530 | ||
| 531 |
# Store in package environment for session tracking (CRAN compliant) |
|
| 532 | 15x |
log_key <- paste0("zse_ferpa_log_", format(timestamp, "%Y%m%d_%H%M%S"))
|
| 533 | 15x |
env <- .zse_get_logs_env() |
| 534 | 15x |
current <- env$logs |
| 535 | 15x |
current[[log_key]] <- log_entry |
| 536 | 15x |
env$logs <- current |
| 537 | ||
| 538 |
# Optionally write to file if logging is enabled |
|
| 539 | 15x |
log_file <- getOption("zoomstudentengagement.ferpa_log_file", NULL)
|
| 540 | 15x |
if (!is.null(log_file) && is.character(log_file)) {
|
| 541 | ! |
tryCatch( |
| 542 |
{
|
|
| 543 | ! |
log_line <- paste( |
| 544 | ! |
format(timestamp, "%Y-%m-%d %H:%M:%S"), |
| 545 | ! |
ifelse(compliant, "COMPLIANT", "NON_COMPLIANT"), |
| 546 | ! |
pii_detected, |
| 547 | ! |
institution_type, |
| 548 | ! |
sep = "\t" |
| 549 |
) |
|
| 550 | ! |
write(log_line, file = log_file, append = TRUE) |
| 551 |
}, |
|
| 552 | ! |
error = function(e) {
|
| 553 |
# Silently fail if logging fails |
|
| 554 | ! |
NULL |
| 555 |
} |
|
| 556 |
) |
|
| 557 |
} |
|
| 558 | ||
| 559 | 15x |
invisible(log_entry) |
| 560 |
} |
| 1 |
#' Load Session Mapping |
|
| 2 |
#' |
|
| 3 |
#' This function loads a session mapping file created by `create_session_mapping()` |
|
| 4 |
#' and integrates it with the Zoom recordings data to provide reliable course |
|
| 5 |
#' information for analysis. |
|
| 6 |
#' |
|
| 7 |
#' @param mapping_file Path to the session mapping CSV file |
|
| 8 |
#' @param zoom_recordings_df Optional Zoom recordings tibble to merge with mapping |
|
| 9 |
#' @param validate_mapping If TRUE, validates that all recordings are properly mapped |
|
| 10 |
#' |
|
| 11 |
#' @return A tibble with the session mapping merged with Zoom recordings data |
|
| 12 |
#' |
|
| 13 |
#' @export |
|
| 14 |
#' |
|
| 15 |
#' @examples |
|
| 16 |
#' \dontrun{
|
|
| 17 |
#' # Load session mapping |
|
| 18 |
#' session_mapping <- load_session_mapping("session_mapping.csv")
|
|
| 19 |
#' |
|
| 20 |
#' # Load and merge with Zoom recordings |
|
| 21 |
#' zoom_recordings_df <- load_zoom_recorded_sessions_list() |
|
| 22 |
#' mapped_recordings <- load_session_mapping( |
|
| 23 |
#' "session_mapping.csv", |
|
| 24 |
#' zoom_recordings_df = zoom_recordings_df |
|
| 25 |
#' ) |
|
| 26 |
#' } |
|
| 27 |
load_session_mapping <- function( |
|
| 28 |
mapping_file = NULL, |
|
| 29 |
zoom_recordings_df = NULL, |
|
| 30 |
validate_mapping = TRUE) {
|
|
| 31 |
# Declare global variables to avoid R CMD check warnings |
|
| 32 | 10x |
zoom_recording_id <- topic <- notes <- dept.x <- instructor.x <- session_date <- NULL |
| 33 | ||
| 34 |
# Check if mapping file exists |
|
| 35 | 10x |
if (!file.exists(mapping_file)) {
|
| 36 | 1x |
abort_zse(paste0("Session mapping file not found: ", mapping_file), class = "zse_input_error")
|
| 37 |
} |
|
| 38 | ||
| 39 |
# Load mapping file with proper column types (flexible for optional columns) |
|
| 40 | 9x |
mapping_df <- readr::read_csv( |
| 41 | 9x |
mapping_file, |
| 42 | 9x |
show_col_types = FALSE, |
| 43 | 9x |
col_types = readr::cols( |
| 44 | 9x |
zoom_recording_id = readr::col_character(), |
| 45 | 9x |
dept = readr::col_character(), |
| 46 | 9x |
course = readr::col_character(), |
| 47 | 9x |
section = readr::col_character(), |
| 48 | 9x |
session_date = readr::col_date(), |
| 49 | 9x |
session_time = readr::col_character(), |
| 50 | 9x |
instructor = readr::col_character(), |
| 51 | 9x |
.default = readr::col_character() |
| 52 |
) |
|
| 53 |
) |
|
| 54 | ||
| 55 |
# Validate required columns |
|
| 56 | 9x |
required_cols <- c( |
| 57 | 9x |
"zoom_recording_id", "dept", "course", "section", |
| 58 | 9x |
"session_date", "session_time", "instructor" |
| 59 |
) |
|
| 60 | 9x |
missing_cols <- setdiff(required_cols, names(mapping_df)) |
| 61 | 9x |
if (length(missing_cols) > 0) {
|
| 62 | 1x |
abort_zse( |
| 63 | 1x |
paste0("Session mapping file missing required columns: ", paste(missing_cols, collapse = ", ")),
|
| 64 | 1x |
class = "zse_schema_error" |
| 65 |
) |
|
| 66 |
} |
|
| 67 | ||
| 68 |
# Validate mapping if requested using base R instead of dplyr to avoid segmentation fault |
|
| 69 | 8x |
if (validate_mapping) {
|
| 70 |
# Use base R subsetting instead of dplyr::filter to avoid segmentation fault |
|
| 71 | 7x |
unmapped_indices <- which(is.na(mapping_df$dept) | is.na(mapping_df$course) | is.na(mapping_df$section)) |
| 72 | 7x |
unmapped <- mapping_df[unmapped_indices, , drop = FALSE] |
| 73 | ||
| 74 | 7x |
if (nrow(unmapped) > 0) {
|
| 75 |
# Only show warnings if not in test environment |
|
| 76 | 2x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 77 | 1x |
warning("Found ", nrow(unmapped), " unmapped recordings in session mapping file")
|
| 78 | 1x |
diag_cat("Unmapped recordings:\n")
|
| 79 |
# Use base R subsetting instead of dplyr::select |
|
| 80 | 1x |
if (is_verbose()) {
|
| 81 | ! |
diag_message(paste( |
| 82 | ! |
utils::capture.output(str(unmapped[, c("zoom_recording_id", "topic", "notes")])),
|
| 83 | ! |
collapse = "\n" |
| 84 |
)) |
|
| 85 |
} |
|
| 86 |
} |
|
| 87 |
} |
|
| 88 |
} |
|
| 89 | ||
| 90 |
# Merge with Zoom recordings if provided using base R instead of dplyr |
|
| 91 | 8x |
if (!is.null(zoom_recordings_df)) {
|
| 92 | 3x |
if (!tibble::is_tibble(zoom_recordings_df)) {
|
| 93 | 1x |
abort_zse("zoom_recordings_df must be a tibble", class = "zse_input_error")
|
| 94 |
} |
|
| 95 | ||
| 96 |
# Ensure ID column exists in zoom_recordings_df |
|
| 97 | 2x |
if (!"ID" %in% names(zoom_recordings_df)) {
|
| 98 | 1x |
abort_zse("zoom_recordings_df must contain 'ID' column", class = "zse_schema_error")
|
| 99 |
} |
|
| 100 | ||
| 101 |
# Merge mapping with recordings using base R instead of dplyr to avoid segmentation fault |
|
| 102 |
# Convert to data.frame for base R operations |
|
| 103 | 1x |
zoom_df <- as.data.frame(zoom_recordings_df) |
| 104 | 1x |
mapping_data <- as.data.frame(mapping_df) |
| 105 | ||
| 106 |
# Perform left join using base R merge |
|
| 107 | 1x |
result <- merge( |
| 108 | 1x |
zoom_df, |
| 109 | 1x |
mapping_data, |
| 110 | 1x |
by.x = "ID", |
| 111 | 1x |
by.y = "zoom_recording_id", |
| 112 | 1x |
all.x = TRUE |
| 113 |
) |
|
| 114 | ||
| 115 |
# Add missing columns if they don't exist |
|
| 116 | ! |
if (!"dept" %in% names(result)) result$dept <- NA_character_ |
| 117 | ! |
if (!"course" %in% names(result)) result$course <- NA_character_ |
| 118 | ! |
if (!"section" %in% names(result)) result$section <- NA_character_ |
| 119 | ! |
if (!"instructor" %in% names(result)) result$instructor <- NA_character_ |
| 120 | ||
| 121 |
# Handle column name conflicts by ensuring mapping data takes precedence |
|
| 122 |
# If both zoom recordings and mapping have the same column, mapping wins |
|
| 123 | 1x |
mapping_cols <- c("dept", "course", "section", "instructor", "session_date", "session_time", "topic", "notes")
|
| 124 | 1x |
for (col in mapping_cols) {
|
| 125 | 8x |
if (col %in% names(mapping_data) && col %in% names(zoom_df)) {
|
| 126 |
# The merge will create col.x (from zoom_df) and col.y (from mapping_data) |
|
| 127 |
# We want to keep the mapping data (col.y) and remove col.x |
|
| 128 | ! |
col_x <- paste0(col, ".x") |
| 129 | ! |
col_y <- paste0(col, ".y") |
| 130 | ! |
if (col_x %in% names(result) && col_y %in% names(result)) {
|
| 131 | ! |
result[[col]] <- result[[col_y]] |
| 132 | ! |
result[[col_x]] <- NULL |
| 133 | ! |
result[[col_y]] <- NULL |
| 134 |
} |
|
| 135 |
} |
|
| 136 |
} |
|
| 137 | ||
| 138 |
# Add computed columns with proper NA handling |
|
| 139 | 1x |
result$course_section <- if (all(c("course", "section") %in% names(result))) {
|
| 140 |
# Handle NA values properly |
|
| 141 | 1x |
course_vals <- ifelse(is.na(result$course), NA_character_, as.character(result$course)) |
| 142 | 1x |
section_vals <- ifelse(is.na(result$section), NA_character_, as.character(result$section)) |
| 143 | ||
| 144 |
# Create course_section only when both course and section are not NA |
|
| 145 | 1x |
course_section_vals <- rep(NA_character_, nrow(result)) |
| 146 | 1x |
valid_indices <- !is.na(course_vals) & !is.na(section_vals) |
| 147 | 1x |
course_section_vals[valid_indices] <- paste(course_vals[valid_indices], section_vals[valid_indices], sep = ".") |
| 148 | 1x |
course_section_vals |
| 149 |
} else {
|
|
| 150 | ! |
rep(NA_character_, nrow(result)) |
| 151 |
} |
|
| 152 | ||
| 153 | 1x |
result$match_start_time <- if ("session_date" %in% names(result)) {
|
| 154 | 1x |
result$session_date |
| 155 |
} else {
|
|
| 156 | ! |
rep(NA, nrow(result)) |
| 157 |
} |
|
| 158 | ||
| 159 | 1x |
result$match_end_time <- if ("session_date" %in% names(result)) {
|
| 160 |
# Handle NA session_date values |
|
| 161 | 1x |
end_times <- rep(as.POSIXct(NA), nrow(result)) |
| 162 | 1x |
valid_indices <- !is.na(result$session_date) |
| 163 | 1x |
if (any(valid_indices)) {
|
| 164 | 1x |
end_times[valid_indices] <- as.POSIXct(result$session_date[valid_indices]) + lubridate::duration(1.5, "hours") |
| 165 |
} |
|
| 166 | 1x |
end_times |
| 167 |
} else {
|
|
| 168 | ! |
rep(as.POSIXct(NA), nrow(result)) |
| 169 |
} |
|
| 170 | ||
| 171 |
# Ensure character columns remain character |
|
| 172 | 1x |
if ("course" %in% names(result)) {
|
| 173 | 1x |
result$course <- as.character(result$course) |
| 174 |
} |
|
| 175 | 1x |
if ("section" %in% names(result)) {
|
| 176 | 1x |
result$section <- as.character(result$section) |
| 177 |
} |
|
| 178 | 1x |
if ("dept" %in% names(result)) {
|
| 179 | 1x |
result$dept <- as.character(result$dept) |
| 180 |
} |
|
| 181 | 1x |
if ("instructor" %in% names(result)) {
|
| 182 | 1x |
result$instructor <- as.character(result$instructor) |
| 183 |
} |
|
| 184 | ||
| 185 |
# Remove unwanted columns using base R |
|
| 186 | 1x |
cols_to_remove <- c("dept_zoom", "session_date", "session_time")
|
| 187 | 1x |
result <- result[, !names(result) %in% cols_to_remove, drop = FALSE] |
| 188 | ||
| 189 |
# Convert back to tibble |
|
| 190 | 1x |
result <- tibble::as_tibble(result) |
| 191 | ||
| 192 | 1x |
return(result) |
| 193 |
} |
|
| 194 | ||
| 195 |
# Return just the mapping if no recordings provided |
|
| 196 | 5x |
tibble::as_tibble(mapping_df) |
| 197 |
} |
| 1 |
#' Write Metrics |
|
| 2 |
#' |
|
| 3 |
#' Unified writer for engagement-related outputs with privacy enforcement. |
|
| 4 |
#' |
|
| 5 |
#' @param data A tibble to write. |
|
| 6 |
#' @param what One of c("engagement", "summary", "session_summary"). Controls default filename.
|
|
| 7 |
#' @param path Output file path. If missing, a default name is chosen based on `what` in the current dir. |
|
| 8 |
#' Parent directories are created if they do not exist. |
|
| 9 |
#' @param comments_format For list-like `comments` columns: one of c("text", "count"). Default: "text".
|
|
| 10 |
#' @param privacy_level Privacy level forwarded to `ensure_privacy()`. Default from option. |
|
| 11 |
#' |
|
| 12 |
#' @return Invisibly returns the written tibble (after privacy transformations and list conversions). |
|
| 13 |
#' @export |
|
| 14 |
write_metrics <- function( |
|
| 15 |
data = NULL, |
|
| 16 |
what = c("engagement", "summary", "session_summary"),
|
|
| 17 |
path = NULL, |
|
| 18 |
comments_format = c("text", "count"),
|
|
| 19 |
privacy_level = getOption("zoomstudentengagement.privacy_level", "mask")) {
|
|
| 20 | 29x |
what <- match.arg(what) |
| 21 | 29x |
comments_format <- match.arg(comments_format) |
| 22 | ||
| 23 | 29x |
if (!tibble::is_tibble(data)) {
|
| 24 | ! |
stop("`data` must be a tibble")
|
| 25 |
} |
|
| 26 | ||
| 27 |
# Enforce privacy (name masking) |
|
| 28 | 29x |
export_data <- zoomstudentengagement::ensure_privacy(data, privacy_level = privacy_level) |
| 29 | ||
| 30 |
# Handle list columns: specially treat `comments` |
|
| 31 | 29x |
if ("comments" %in% names(export_data) && is.list(export_data$comments)) {
|
| 32 | 16x |
if (comments_format == "text") {
|
| 33 | 12x |
export_data$comments <- vapply(export_data$comments, function(x) {
|
| 34 | 89x |
if (is.null(x) || length(x) == 0) {
|
| 35 | 2x |
return("")
|
| 36 |
} |
|
| 37 | 87x |
paste(unlist(x), collapse = "; ") |
| 38 | 12x |
}, FUN.VALUE = character(1)) |
| 39 |
} else {
|
|
| 40 | 4x |
export_data$comments <- vapply(export_data$comments, function(x) {
|
| 41 | 8x |
if (is.null(x)) {
|
| 42 | 1x |
return(0L) |
| 43 |
} |
|
| 44 | 7x |
length(unlist(x)) |
| 45 | 4x |
}, FUN.VALUE = integer(1)) |
| 46 |
} |
|
| 47 |
} |
|
| 48 | ||
| 49 | 29x |
list_columns <- vapply(export_data, is.list, logical(1)) |
| 50 | 29x |
if (any(list_columns)) {
|
| 51 | 3x |
list_col_names <- names(export_data)[list_columns] |
| 52 | 3x |
warning("Converting list columns to JSON strings: ", paste(list_col_names, collapse = ", "))
|
| 53 | 3x |
for (col in list_col_names) {
|
| 54 | 4x |
export_data[[col]] <- vapply(export_data[[col]], function(x) {
|
| 55 | 8x |
if (is.null(x) || length(x) == 0) {
|
| 56 | 1x |
return("")
|
| 57 |
} |
|
| 58 | 7x |
jsonlite::toJSON(x, auto_unbox = TRUE) |
| 59 | 4x |
}, FUN.VALUE = character(1)) |
| 60 |
} |
|
| 61 |
} |
|
| 62 | ||
| 63 |
# Determine default filename |
|
| 64 | 29x |
if (is.null(path)) {
|
| 65 | ! |
fname <- switch(what, |
| 66 | ! |
engagement = "engagement_metrics.csv", |
| 67 | ! |
summary = "transcripts_summary.csv", |
| 68 | ! |
session_summary = "transcripts_session_summary.csv" |
| 69 |
) |
|
| 70 | ! |
path <- fname |
| 71 |
} |
|
| 72 | 29x |
dir_path <- dirname(path) |
| 73 | 29x |
if (!dir.exists(dir_path)) {
|
| 74 | 2x |
dir.create(dir_path, recursive = TRUE) |
| 75 |
} |
|
| 76 | 29x |
utils::write.csv(export_data, path, row.names = FALSE) |
| 77 | 28x |
invisible(export_data) |
| 78 |
} |
| 1 |
#' Ensure Privacy for Outputs |
|
| 2 |
#' |
|
| 3 |
#' Applies privacy rules to objects before they are returned, written, or |
|
| 4 |
#' plotted. By default, masks personally identifiable information in tabular |
|
| 5 |
#' data to FERPA-safe placeholders. |
|
| 6 |
#' |
|
| 7 |
#' **CRITICAL ETHICAL COMPLIANCE**: This function is designed to promote |
|
| 8 |
#' participation equity and educational improvement, NOT surveillance. All |
|
| 9 |
#' outputs are automatically anonymized by default to protect student privacy |
|
| 10 |
#' and ensure FERPA compliance. |
|
| 11 |
#' |
|
| 12 |
#' The default behavior is controlled by the global option |
|
| 13 |
#' `zoomstudentengagement.privacy_level`, which is set to "mask" on package |
|
| 14 |
#' load. Use `set_privacy_defaults()` to change at runtime. |
|
| 15 |
#' |
|
| 16 |
#' @param x An object to make privacy-safe. Currently supports `data.frame` or |
|
| 17 |
#' `tibble`. Other object types are returned unchanged. |
|
| 18 |
#' @param privacy_level Privacy level to apply. One of `c("ferpa_strict", "ferpa_standard", "mask", "none")`.
|
|
| 19 |
#' Defaults to `getOption("zoomstudentengagement.privacy_level", "mask")`.
|
|
| 20 |
#' **WARNING**: Setting to "none" disables privacy protection and may violate |
|
| 21 |
#' FERPA requirements. |
|
| 22 |
#' @param id_columns Character vector of column names to treat as identifiers. |
|
| 23 |
#' Defaults to common name/identifier columns. |
|
| 24 |
#' @param audit_log Whether to log privacy operations for compliance tracking. |
|
| 25 |
#' Defaults to TRUE for maximum transparency. |
|
| 26 |
#' |
|
| 27 |
#' @return The object with privacy rules applied. For data frames, the same |
|
| 28 |
#' structure is preserved with identifying fields masked when appropriate. |
|
| 29 |
#' |
|
| 30 |
#' @seealso [set_privacy_defaults()], [validate_ethical_use()] |
|
| 31 |
#' @export |
|
| 32 |
#' |
|
| 33 |
#' @examples |
|
| 34 |
#' # Data frame masking example |
|
| 35 |
#' df <- tibble::tibble( |
|
| 36 |
#' section = c("A", "A", "B"),
|
|
| 37 |
#' preferred_name = c("Alice Johnson", "Bob Lee", "Cara Diaz"),
|
|
| 38 |
#' session_ct = c(3, 5, 2) |
|
| 39 |
#' ) |
|
| 40 |
#' ensure_privacy(df) |
|
| 41 |
ensure_privacy <- function(x = NULL, |
|
| 42 |
privacy_level = getOption( |
|
| 43 |
"zoomstudentengagement.privacy_level", |
|
| 44 |
"mask" |
|
| 45 |
), |
|
| 46 |
id_columns = c( |
|
| 47 |
"preferred_name", "name", "first_last", |
|
| 48 |
"name_raw", "student_id", "email", "transcript_name", "formal_name" |
|
| 49 |
), |
|
| 50 |
audit_log = TRUE) {
|
|
| 51 |
# Validate privacy level |
|
| 52 | 337x |
valid_levels <- c("ferpa_strict", "ferpa_standard", "mask", "none")
|
| 53 | 337x |
if (!privacy_level %in% valid_levels) {
|
| 54 | 1x |
stop("Invalid privacy_level. Must be one of: ", paste(valid_levels, collapse = ", "), call. = FALSE)
|
| 55 |
} |
|
| 56 | ||
| 57 |
# If privacy is explicitly disabled, warn and return unmodified |
|
| 58 | 336x |
if (identical(privacy_level, "none")) {
|
| 59 | 8x |
warning( |
| 60 | 8x |
"CRITICAL: Privacy disabled; outputs may contain identifiable data and violate FERPA requirements.", |
| 61 | 8x |
call. = FALSE |
| 62 |
) |
|
| 63 | ||
| 64 |
# Log the privacy violation for audit purposes |
|
| 65 | 8x |
if (audit_log) {
|
| 66 | 8x |
log_privacy_operation( |
| 67 | 8x |
operation = "privacy_disabled", |
| 68 | 8x |
privacy_level = privacy_level, |
| 69 | 8x |
timestamp = Sys.time(), |
| 70 | 8x |
warning_issued = TRUE |
| 71 |
) |
|
| 72 |
} |
|
| 73 | ||
| 74 | 8x |
return(x) |
| 75 |
} |
|
| 76 | ||
| 77 |
# FERPA strict level - most comprehensive masking |
|
| 78 | 328x |
if (identical(privacy_level, "ferpa_strict")) {
|
| 79 | 140x |
id_columns <- c( |
| 80 | 140x |
id_columns, |
| 81 | 140x |
"email", "email_address", "e_mail", |
| 82 | 140x |
"phone", "phone_number", "telephone", |
| 83 | 140x |
"address", "street_address", "home_address", |
| 84 | 140x |
"ssn", "social_security", "social_security_number", |
| 85 | 140x |
"birth_date", "birthday", "date_of_birth", |
| 86 | 140x |
"parent_name", "guardian_name", |
| 87 | 140x |
"instructor_name", "instructor_id" |
| 88 |
) |
|
| 89 |
} |
|
| 90 | ||
| 91 |
# FERPA standard level - standard educational compliance |
|
| 92 | 328x |
if (identical(privacy_level, "ferpa_standard")) {
|
| 93 | 3x |
id_columns <- c( |
| 94 | 3x |
id_columns, |
| 95 | 3x |
"email", "email_address", "e_mail", |
| 96 | 3x |
"phone", "phone_number", "telephone", |
| 97 | 3x |
"instructor_name", "instructor_id" |
| 98 |
) |
|
| 99 |
} |
|
| 100 | ||
| 101 |
# Only handle tabular data for MVP; return other objects unchanged |
|
| 102 | 328x |
if (!is.data.frame(x)) {
|
| 103 | ! |
return(x) |
| 104 |
} |
|
| 105 | ||
| 106 |
# Log privacy operation for audit purposes |
|
| 107 | 328x |
if (audit_log) {
|
| 108 | 328x |
log_privacy_operation( |
| 109 | 328x |
operation = "privacy_applied", |
| 110 | 328x |
privacy_level = privacy_level, |
| 111 | 328x |
data_rows = nrow(x), |
| 112 | 328x |
data_columns = ncol(x), |
| 113 | 328x |
timestamp = Sys.time() |
| 114 |
) |
|
| 115 |
} |
|
| 116 | ||
| 117 | 328x |
df <- x |
| 118 | ||
| 119 |
# Identify columns to mask while preserving structure |
|
| 120 | 328x |
columns_to_mask <- intersect(id_columns, names(df)) |
| 121 | 328x |
if (length(columns_to_mask) == 0) {
|
| 122 |
# Nothing to mask; return as-is |
|
| 123 | 18x |
if (tibble::is_tibble(x)) {
|
| 124 | 12x |
return(tibble::as_tibble(df)) |
| 125 |
} else {
|
|
| 126 | 6x |
return(df) |
| 127 |
} |
|
| 128 |
} |
|
| 129 | ||
| 130 | 310x |
mask_values <- function(values) {
|
| 131 |
# Convert to character for stable mapping; preserve NAs and empty strings |
|
| 132 | 364x |
chr <- as.character(values) |
| 133 |
# Unique non-empty, non-NA values for deterministic mapping |
|
| 134 | 364x |
unique_vals <- unique(chr[!is.na(chr) & nzchar(chr)]) |
| 135 | 364x |
unique_vals <- sort(unique_vals) |
| 136 | 364x |
if (length(unique_vals) == 0) {
|
| 137 | 13x |
return(chr) |
| 138 |
} |
|
| 139 | 351x |
labels <- paste( |
| 140 | 351x |
"Student", |
| 141 | 351x |
stringr::str_pad(seq_along(unique_vals), width = 2, pad = "0") |
| 142 |
) |
|
| 143 | 351x |
mapping <- stats::setNames(labels, unique_vals) |
| 144 | 351x |
to_mask <- !is.na(chr) & nzchar(chr) |
| 145 | 351x |
chr[to_mask] <- unname(mapping[chr[to_mask]]) |
| 146 | 351x |
chr |
| 147 |
} |
|
| 148 | ||
| 149 | 310x |
for (col_name in columns_to_mask) {
|
| 150 |
# Mask only character or factor columns |
|
| 151 | 364x |
if (is.character(df[[col_name]]) || is.factor(df[[col_name]])) {
|
| 152 | 364x |
df[[col_name]] <- mask_values(df[[col_name]]) |
| 153 |
} |
|
| 154 |
} |
|
| 155 | ||
| 156 |
# Preserve tibble class if input was a tibble |
|
| 157 | 310x |
if (tibble::is_tibble(x)) {
|
| 158 | 306x |
df <- tibble::as_tibble(df) |
| 159 |
} |
|
| 160 | ||
| 161 | 310x |
df |
| 162 |
} |
|
| 163 | ||
| 164 |
#' Log Privacy Operations |
|
| 165 |
#' |
|
| 166 |
#' Internal function to log privacy operations for audit and compliance purposes. |
|
| 167 |
#' This function maintains a record of privacy-related operations for institutional |
|
| 168 |
#' review and FERPA compliance. |
|
| 169 |
#' |
|
| 170 |
#' @param operation Type of operation performed |
|
| 171 |
#' @param privacy_level Privacy level used |
|
| 172 |
#' @param timestamp When the operation occurred |
|
| 173 |
#' @param data_rows Number of rows processed (if applicable) |
|
| 174 |
#' @param data_columns Number of columns processed (if applicable) |
|
| 175 |
#' @param warning_issued Whether a warning was issued |
|
| 176 |
#' |
|
| 177 |
#' @keywords internal |
|
| 178 |
log_privacy_operation <- function(operation, |
|
| 179 |
privacy_level, |
|
| 180 |
timestamp = Sys.time(), |
|
| 181 |
data_rows = NULL, |
|
| 182 |
data_columns = NULL, |
|
| 183 |
warning_issued = FALSE) {
|
|
| 184 |
# DEPRECATED: This function will be removed in the next version |
|
| 185 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 186 | 336x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 187 | ! |
warning("Function 'log_privacy_operation' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 188 |
} |
|
| 189 | ||
| 190 |
# Create log entry |
|
| 191 | 336x |
log_entry <- list( |
| 192 | 336x |
timestamp = timestamp, |
| 193 | 336x |
operation = operation, |
| 194 | 336x |
privacy_level = privacy_level, |
| 195 | 336x |
data_rows = data_rows, |
| 196 | 336x |
data_columns = data_columns, |
| 197 | 336x |
warning_issued = warning_issued, |
| 198 | 336x |
session_id = Sys.getpid() |
| 199 |
) |
|
| 200 | ||
| 201 |
# Store in package environment for session tracking (CRAN compliant) |
|
| 202 | 336x |
log_key <- paste0("zse_privacy_log_", format(timestamp, "%Y%m%d_%H%M%S"))
|
| 203 | 336x |
env <- .zse_get_logs_env() |
| 204 | 336x |
current <- env$logs |
| 205 | 336x |
current[[log_key]] <- log_entry |
| 206 | 336x |
env$logs <- current |
| 207 | ||
| 208 |
# Optionally write to file if logging is enabled |
|
| 209 | 336x |
log_file <- getOption("zoomstudentengagement.privacy_log_file", NULL)
|
| 210 | 336x |
if (!is.null(log_file) && is.character(log_file)) {
|
| 211 | ! |
tryCatch( |
| 212 |
{
|
|
| 213 | ! |
log_line <- paste( |
| 214 | ! |
format(timestamp, "%Y-%m-%d %H:%M:%S"), |
| 215 | ! |
operation, |
| 216 | ! |
privacy_level, |
| 217 | ! |
ifelse(is.null(data_rows), "NA", data_rows), |
| 218 | ! |
ifelse(is.null(data_columns), "NA", data_columns), |
| 219 | ! |
ifelse(warning_issued, "WARNING", "OK"), |
| 220 | ! |
sep = "\t" |
| 221 |
) |
|
| 222 | ! |
write(log_line, file = log_file, append = TRUE) |
| 223 |
}, |
|
| 224 | ! |
error = function(e) {
|
| 225 |
# Silently fail if logging fails |
|
| 226 | ! |
NULL |
| 227 |
} |
|
| 228 |
) |
|
| 229 |
} |
|
| 230 | ||
| 231 | 336x |
invisible(log_entry) |
| 232 |
} |
| 1 |
#' CRAN Optimization System |
|
| 2 |
#' |
|
| 3 |
#' @description Optimizes function set for CRAN submission |
|
| 4 |
#' @keywords internal |
|
| 5 |
#' @noRd |
|
| 6 | ||
| 7 |
#' Select functions for CRAN submission |
|
| 8 |
#' |
|
| 9 |
#' @param function_categories Function categories from audit |
|
| 10 |
#' @param max_functions Maximum number of functions for CRAN (default: 30) |
|
| 11 |
#' @return CRAN-optimized function set |
|
| 12 |
select_cran_functions <- function(function_categories, max_functions = 30) {
|
|
| 13 | ! |
cat("šÆ Selecting functions for CRAN submission...\n")
|
| 14 | ! |
cat("š Target: Maximum", max_functions, "functions\n\n")
|
| 15 | ||
| 16 |
# Priority order for CRAN functions (based on user workflow importance) |
|
| 17 | ! |
priority_order <- c( |
| 18 | ! |
"core_workflow", # Essential for basic transcript analysis |
| 19 | ! |
"privacy_compliance", # Critical for FERPA compliance |
| 20 | ! |
"data_processing", # Core data handling |
| 21 | ! |
"analysis", # Core analysis functions |
| 22 | ! |
"visualization", # Essential plotting and export |
| 23 | ! |
"utility" # Helper functions |
| 24 |
) |
|
| 25 | ||
| 26 | ! |
cran_functions <- character(0) |
| 27 | ! |
category_allocations <- list() |
| 28 | ||
| 29 |
# Allocate functions by category priority |
|
| 30 | ! |
for (category in priority_order) {
|
| 31 | ! |
if (category %in% names(function_categories)) {
|
| 32 | ! |
category_functions <- function_categories[[category]] |
| 33 | ||
| 34 |
# Determine allocation for this category |
|
| 35 | ! |
allocation <- get_category_allocation(category, max_functions, length(cran_functions)) |
| 36 | ||
| 37 | ! |
if (allocation > 0 && length(category_functions) > 0) {
|
| 38 | ! |
functions_to_add <- category_functions[1:min(allocation, length(category_functions))] |
| 39 | ! |
cran_functions <- c(cran_functions, functions_to_add) |
| 40 | ! |
category_allocations[[category]] <- length(functions_to_add) |
| 41 | ||
| 42 | ! |
cat(sprintf( |
| 43 | ! |
"š %-20s: %2d functions allocated\n", |
| 44 | ! |
category, length(functions_to_add) |
| 45 |
)) |
|
| 46 |
} |
|
| 47 |
} |
|
| 48 |
} |
|
| 49 | ||
| 50 |
# Remove any duplicates and limit to max_functions |
|
| 51 | ! |
cran_functions <- unique(cran_functions) |
| 52 | ! |
cran_functions <- cran_functions[1:min(max_functions, length(cran_functions))] |
| 53 | ||
| 54 | ! |
cat(sprintf("\nā
Selected %d functions for CRAN submission\n", length(cran_functions)))
|
| 55 | ||
| 56 | ! |
return(list( |
| 57 | ! |
functions = cran_functions, |
| 58 | ! |
allocations = category_allocations, |
| 59 | ! |
total_selected = length(cran_functions) |
| 60 |
)) |
|
| 61 |
} |
|
| 62 | ||
| 63 |
#' Get category allocation based on priority and remaining slots |
|
| 64 |
#' |
|
| 65 |
#' @param category Category name |
|
| 66 |
#' @param max_functions Maximum total functions |
|
| 67 |
#' @param current_count Current number of selected functions |
|
| 68 |
#' @return Number of functions to allocate to this category |
|
| 69 |
get_category_allocation <- function(category, max_functions, current_count) {
|
|
| 70 |
# Allocation strategy based on category importance |
|
| 71 | ! |
allocations <- list( |
| 72 | ! |
core_workflow = 8, # Essential workflow functions |
| 73 | ! |
privacy_compliance = 6, # Critical for compliance |
| 74 | ! |
data_processing = 6, # Core data handling |
| 75 | ! |
analysis = 5, # Analysis functions |
| 76 | ! |
visualization = 3, # Plotting and export |
| 77 | ! |
utility = 2 # Helper functions |
| 78 |
) |
|
| 79 | ||
| 80 | ! |
if (category %in% names(allocations)) {
|
| 81 | ! |
requested <- allocations[[category]] |
| 82 | ! |
remaining <- max_functions - current_count |
| 83 | ! |
return(min(requested, remaining)) |
| 84 |
} else {
|
|
| 85 | ! |
return(0) |
| 86 |
} |
|
| 87 |
} |
|
| 88 | ||
| 89 |
#' Mark functions for deprecation |
|
| 90 |
#' |
|
| 91 |
#' @param function_categories Function categories from audit |
|
| 92 |
#' @param cran_functions Functions selected for CRAN |
|
| 93 |
#' @return Functions marked for deprecation |
|
| 94 |
mark_deprecated_functions <- function(function_categories, cran_functions) {
|
|
| 95 | ! |
all_functions <- unlist(function_categories) |
| 96 | ! |
deprecated_functions <- setdiff(all_functions, cran_functions) |
| 97 | ||
| 98 | ! |
cat("š Marking functions for deprecation...\n")
|
| 99 | ! |
cat("š Total functions:", length(all_functions), "\n")
|
| 100 | ! |
cat("š CRAN functions:", length(cran_functions), "\n")
|
| 101 | ! |
cat("š Deprecated functions:", length(deprecated_functions), "\n\n")
|
| 102 | ||
| 103 | ! |
return(deprecated_functions) |
| 104 |
} |
|
| 105 | ||
| 106 |
#' Analyze breaking change impact |
|
| 107 |
#' |
|
| 108 |
#' @param deprecated_functions Functions marked for deprecation |
|
| 109 |
#' @param function_analysis Function analysis results |
|
| 110 |
#' @return Breaking change analysis |
|
| 111 |
analyze_breaking_changes <- function(deprecated_functions, function_analysis) {
|
|
| 112 | ! |
cat("ā ļø Analyzing breaking change impact...\n")
|
| 113 | ||
| 114 | ! |
impact_analysis <- list( |
| 115 | ! |
high_impact = character(0), |
| 116 | ! |
medium_impact = character(0), |
| 117 | ! |
low_impact = character(0), |
| 118 | ! |
no_impact = character(0) |
| 119 |
) |
|
| 120 | ||
| 121 | ! |
for (func_name in deprecated_functions) {
|
| 122 | ! |
if (func_name %in% names(function_analysis)) {
|
| 123 | ! |
func_info <- function_analysis[[func_name]] |
| 124 | ||
| 125 |
# Determine impact level based on usage patterns |
|
| 126 | ! |
impact_level <- determine_impact_level(func_info) |
| 127 | ! |
impact_analysis[[impact_level]] <- c(impact_analysis[[impact_level]], func_name) |
| 128 |
} |
|
| 129 |
} |
|
| 130 | ||
| 131 |
# Print impact summary |
|
| 132 | ! |
cat("š Breaking Change Impact Summary:\n")
|
| 133 | ! |
for (impact in names(impact_analysis)) {
|
| 134 | ! |
count <- length(impact_analysis[[impact]]) |
| 135 | ! |
cat(sprintf(" %-12s: %2d functions\n", impact, count))
|
| 136 |
} |
|
| 137 | ! |
cat("\n")
|
| 138 | ||
| 139 | ! |
return(impact_analysis) |
| 140 |
} |
|
| 141 | ||
| 142 |
#' Determine impact level of deprecating a function |
|
| 143 |
#' |
|
| 144 |
#' @param func_info Function analysis information |
|
| 145 |
#' @return Impact level |
|
| 146 |
determine_impact_level <- function(func_info) {
|
|
| 147 | ! |
usage <- func_info$usage |
| 148 | ! |
documentation <- func_info$documentation |
| 149 | ||
| 150 |
# High impact: functions with examples and tests |
|
| 151 | ! |
if (usage$in_examples && usage$in_tests && documentation == "Complete") {
|
| 152 | ! |
return("high_impact")
|
| 153 |
} |
|
| 154 | ||
| 155 |
# Medium impact: functions with either examples or tests |
|
| 156 | ! |
if ((usage$in_examples || usage$in_tests) && documentation == "Complete") {
|
| 157 | ! |
return("medium_impact")
|
| 158 |
} |
|
| 159 | ||
| 160 |
# Low impact: functions with documentation but no examples/tests |
|
| 161 | ! |
if (documentation == "Complete") {
|
| 162 | ! |
return("low_impact")
|
| 163 |
} |
|
| 164 | ||
| 165 |
# No impact: functions without proper documentation |
|
| 166 | ! |
return("no_impact")
|
| 167 |
} |
|
| 168 | ||
| 169 |
#' Generate migration recommendations |
|
| 170 |
#' |
|
| 171 |
#' @param deprecated_functions Functions marked for deprecation |
|
| 172 |
#' @param cran_functions Functions selected for CRAN |
|
| 173 |
#' @param function_analysis Function analysis results |
|
| 174 |
#' @return Migration recommendations |
|
| 175 |
generate_migration_recommendations <- function(deprecated_functions, cran_functions, function_analysis) {
|
|
| 176 | ! |
cat("š Generating migration recommendations...\n")
|
| 177 | ||
| 178 | ! |
migration_guide <- list() |
| 179 | ||
| 180 | ! |
for (func_name in deprecated_functions) {
|
| 181 | ! |
if (func_name %in% names(function_analysis)) {
|
| 182 | ! |
func_info <- function_analysis[[func_name]] |
| 183 | ||
| 184 |
# Find potential replacement function |
|
| 185 | ! |
replacement <- find_replacement_function(func_name, cran_functions, function_analysis) |
| 186 | ||
| 187 | ! |
migration_guide[[func_name]] <- list( |
| 188 | ! |
original_function = func_name, |
| 189 | ! |
replacement_function = replacement, |
| 190 | ! |
migration_strategy = get_migration_strategy(func_name, replacement), |
| 191 | ! |
impact_level = determine_impact_level(func_info) |
| 192 |
) |
|
| 193 |
} |
|
| 194 |
} |
|
| 195 | ||
| 196 | ! |
return(migration_guide) |
| 197 |
} |
|
| 198 | ||
| 199 |
#' Find replacement function for deprecated function |
|
| 200 |
#' |
|
| 201 |
#' @param deprecated_func Deprecated function name |
|
| 202 |
#' @param cran_functions Available CRAN functions |
|
| 203 |
#' @param function_analysis Function analysis results |
|
| 204 |
#' @return Suggested replacement function |
|
| 205 |
find_replacement_function <- function(deprecated_func, cran_functions, function_analysis) {
|
|
| 206 |
# Simple heuristic-based replacement suggestions |
|
| 207 | ! |
replacements <- list( |
| 208 |
# Common patterns |
|
| 209 | ! |
"make_" = "create_", |
| 210 | ! |
"generate_" = "create_", |
| 211 | ! |
"write_" = "export_", |
| 212 | ! |
"plot_" = "visualize_" |
| 213 |
) |
|
| 214 | ||
| 215 | ! |
for (pattern in names(replacements)) {
|
| 216 | ! |
if (grepl(pattern, deprecated_func)) {
|
| 217 | ! |
potential_replacement <- gsub(pattern, replacements[[pattern]], deprecated_func) |
| 218 | ! |
if (potential_replacement %in% cran_functions) {
|
| 219 | ! |
return(potential_replacement) |
| 220 |
} |
|
| 221 |
} |
|
| 222 |
} |
|
| 223 | ||
| 224 |
# Look for functions with similar names |
|
| 225 | ! |
similar_functions <- cran_functions[grepl(gsub("_.*", "", deprecated_func), cran_functions)]
|
| 226 | ! |
if (length(similar_functions) > 0) {
|
| 227 | ! |
return(similar_functions[1]) |
| 228 |
} |
|
| 229 | ||
| 230 | ! |
return("No direct replacement available")
|
| 231 |
} |
|
| 232 | ||
| 233 |
#' Get migration strategy for deprecated function |
|
| 234 |
#' |
|
| 235 |
#' @param deprecated_func Deprecated function name |
|
| 236 |
#' @param replacement Suggested replacement |
|
| 237 |
#' @return Migration strategy |
|
| 238 |
get_migration_strategy <- function(deprecated_func, replacement) {
|
|
| 239 | ! |
if (replacement == "No direct replacement available") {
|
| 240 | ! |
return("Function will be removed. Consider alternative approaches or contact maintainers.")
|
| 241 |
} else {
|
|
| 242 | ! |
return(paste("Replace", deprecated_func, "with", replacement, "and update function calls."))
|
| 243 |
} |
|
| 244 |
} |
|
| 245 | ||
| 246 |
#' Validate CRAN optimization results |
|
| 247 |
#' |
|
| 248 |
#' @param cran_selection CRAN function selection results |
|
| 249 |
#' @param deprecated_functions Functions marked for deprecation |
|
| 250 |
#' @param function_analysis Function analysis results |
|
| 251 |
#' @return Validation results |
|
| 252 |
validate_cran_optimization <- function(cran_selection, deprecated_functions, function_analysis) {
|
|
| 253 | ! |
cat("ā
Validating CRAN optimization results...\n")
|
| 254 | ||
| 255 | ! |
validation_results <- list( |
| 256 | ! |
function_count_valid = length(cran_selection$functions) <= 30, |
| 257 | ! |
no_duplicates = length(cran_selection$functions) == length(unique(cran_selection$functions)), |
| 258 | ! |
all_cran_functions_documented = TRUE, |
| 259 | ! |
migration_path_available = length(deprecated_functions) > 0 |
| 260 |
) |
|
| 261 | ||
| 262 |
# Check documentation for CRAN functions |
|
| 263 | ! |
for (func_name in cran_selection$functions) {
|
| 264 | ! |
if (func_name %in% names(function_analysis)) {
|
| 265 | ! |
if (function_analysis[[func_name]]$documentation != "Complete") {
|
| 266 | ! |
validation_results$all_cran_functions_documented <- FALSE |
| 267 | ! |
break |
| 268 |
} |
|
| 269 |
} |
|
| 270 |
} |
|
| 271 | ||
| 272 |
# Print validation results |
|
| 273 | ! |
cat("š Validation Results:\n")
|
| 274 | ! |
for (check in names(validation_results)) {
|
| 275 | ! |
status <- if (validation_results[[check]]) "ā " else "ā" |
| 276 | ! |
cat(sprintf(" %-30s: %s\n", check, status))
|
| 277 |
} |
|
| 278 | ! |
cat("\n")
|
| 279 | ||
| 280 | ! |
return(validation_results) |
| 281 |
} |
|
| 282 | ||
| 283 |
#' Test CRAN optimization system |
|
| 284 |
#' |
|
| 285 |
#' @return Test results |
|
| 286 |
test_cran_optimization <- function() {
|
|
| 287 | ! |
cat("š§Ŗ Testing CRAN optimization system...\n")
|
| 288 | ||
| 289 |
# Test with sample categories |
|
| 290 | ! |
sample_categories <- list( |
| 291 | ! |
core_workflow = c("analyze_transcripts", "load_zoom_transcript", "process_zoom_transcript"),
|
| 292 | ! |
privacy_compliance = c("privacy_audit", "ensure_privacy", "validate_ferpa_compliance"),
|
| 293 | ! |
data_processing = c("consolidate_transcript", "detect_duplicate_transcripts"),
|
| 294 | ! |
analysis = c("summarize_transcript_metrics", "generate_attendance_report"),
|
| 295 | ! |
visualization = c("plot_users", "write_metrics"),
|
| 296 | ! |
utility = c("get_essential_functions", "set_privacy_defaults")
|
| 297 |
) |
|
| 298 | ||
| 299 | ! |
cran_selection <- select_cran_functions(sample_categories, max_functions = 15) |
| 300 | ! |
deprecated_functions <- mark_deprecated_functions(sample_categories, cran_selection$functions) |
| 301 | ||
| 302 | ! |
cat("ā
CRAN optimization test completed\n")
|
| 303 | ||
| 304 | ! |
return(list( |
| 305 | ! |
cran_selection = cran_selection, |
| 306 | ! |
deprecated_functions = deprecated_functions |
| 307 |
)) |
|
| 308 |
} |
| 1 |
#' Export Ideal Course Transcripts to CSV |
|
| 2 |
#' |
|
| 3 |
#' Exports ideal course transcript data to CSV format with privacy protection |
|
| 4 |
#' and comprehensive metadata. |
|
| 5 |
#' |
|
| 6 |
#' @param transcript_data Data frame containing transcript data |
|
| 7 |
#' @param file_path Character. Output file path. If NULL, generates default name |
|
| 8 |
#' @param privacy_level Character. Privacy level for data masking. Default from option |
|
| 9 |
#' @param include_metadata Logical. Whether to include metadata in export. Default: TRUE |
|
| 10 |
#' @return Invisibly returns the exported data frame |
|
| 11 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 12 |
#' @examples |
|
| 13 |
#' \dontrun{
|
|
| 14 |
#' # Export with default settings |
|
| 15 |
#' export_ideal_transcripts_csv(transcript_data) |
|
| 16 |
#' |
|
| 17 |
#' # Export with custom privacy level |
|
| 18 |
#' export_ideal_transcripts_csv( |
|
| 19 |
#' transcript_data, |
|
| 20 |
#' file_path = "my_transcript.csv", |
|
| 21 |
#' privacy_level = "full" |
|
| 22 |
#' ) |
|
| 23 |
#' } |
|
| 24 |
#' @export |
|
| 25 |
#' @keywords deprecated |
|
| 26 |
export_ideal_transcripts_csv <- function( |
|
| 27 |
transcript_data = NULL, |
|
| 28 |
file_path = NULL, |
|
| 29 |
privacy_level = getOption("zoomstudentengagement.privacy_level", "mask"),
|
|
| 30 |
include_metadata = TRUE) {
|
|
| 31 |
# DEPRECATED: This function will be removed in the next version |
|
| 32 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 33 | 16x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 34 | ! |
warning("Function 'export_ideal_transcripts_csv' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 35 |
} |
|
| 36 | ||
| 37 |
# Validate inputs |
|
| 38 | 16x |
if (is.null(transcript_data)) {
|
| 39 | 1x |
stop("transcript_data cannot be NULL")
|
| 40 |
} |
|
| 41 | ||
| 42 | 15x |
if (!tibble::is_tibble(transcript_data) && !is.data.frame(transcript_data)) {
|
| 43 | 1x |
stop("transcript_data must be a tibble or data frame")
|
| 44 |
} |
|
| 45 | ||
| 46 |
# Apply privacy protection |
|
| 47 | 14x |
export_data <- zoomstudentengagement::ensure_privacy( |
| 48 | 14x |
transcript_data, |
| 49 | 14x |
privacy_level = privacy_level |
| 50 |
) |
|
| 51 | ||
| 52 |
# Add metadata if requested |
|
| 53 | 14x |
if (include_metadata) {
|
| 54 | 13x |
export_data <- add_export_metadata(export_data, format = "csv") |
| 55 |
} |
|
| 56 | ||
| 57 |
# Determine output path |
|
| 58 | 14x |
if (is.null(file_path)) {
|
| 59 | 1x |
timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S") |
| 60 | 1x |
file_path <- paste0("ideal_transcript_export_", timestamp, ".csv")
|
| 61 |
} |
|
| 62 | ||
| 63 |
# Create directory if needed |
|
| 64 | 14x |
dir_path <- dirname(file_path) |
| 65 | 14x |
if (dir_path != "." && !dir.exists(dir_path)) {
|
| 66 | 1x |
dir.create(dir_path, recursive = TRUE) |
| 67 |
} |
|
| 68 | ||
| 69 |
# Write CSV file |
|
| 70 | 14x |
utils::write.csv(export_data, file_path, row.names = FALSE) |
| 71 | ||
| 72 | 14x |
invisible(file_path) |
| 73 |
} |
|
| 74 | ||
| 75 |
#' Export Ideal Course Transcripts to JSON |
|
| 76 |
#' |
|
| 77 |
#' Exports ideal course transcript data to JSON format with structured output |
|
| 78 |
#' and privacy protection. |
|
| 79 |
#' |
|
| 80 |
#' @param transcript_data Data frame containing transcript data |
|
| 81 |
#' @param file_path Character. Output file path. If NULL, generates default name |
|
| 82 |
#' @param privacy_level Character. Privacy level for data masking. Default from option |
|
| 83 |
#' @param pretty_print Logical. Whether to format JSON with indentation. Default: TRUE |
|
| 84 |
#' @param include_metadata Logical. Whether to include metadata. Default: TRUE |
|
| 85 |
#' @return Invisibly returns the exported data as list |
|
| 86 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 87 |
#' @examples |
|
| 88 |
#' \dontrun{
|
|
| 89 |
#' # Export with default settings |
|
| 90 |
#' export_ideal_transcripts_json(transcript_data) |
|
| 91 |
#' |
|
| 92 |
#' # Export with custom formatting |
|
| 93 |
#' export_ideal_transcripts_json( |
|
| 94 |
#' transcript_data, |
|
| 95 |
#' pretty_print = FALSE, |
|
| 96 |
#' include_metadata = FALSE |
|
| 97 |
#' ) |
|
| 98 |
#' } |
|
| 99 |
#' @export |
|
| 100 |
#' @keywords deprecated |
|
| 101 |
export_ideal_transcripts_json <- function( |
|
| 102 |
transcript_data = NULL, |
|
| 103 |
file_path = NULL, |
|
| 104 |
privacy_level = getOption("zoomstudentengagement.privacy_level", "mask"),
|
|
| 105 |
pretty_print = TRUE, |
|
| 106 |
include_metadata = TRUE) {
|
|
| 107 |
# DEPRECATED: This function will be removed in the next version |
|
| 108 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 109 | 6x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 110 | ! |
warning("Function 'export_ideal_transcripts_json' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 111 |
} |
|
| 112 | ||
| 113 |
# Validate inputs |
|
| 114 | 6x |
if (is.null(transcript_data)) {
|
| 115 | 1x |
stop("transcript_data cannot be NULL")
|
| 116 |
} |
|
| 117 | ||
| 118 | 5x |
if (!tibble::is_tibble(transcript_data) && !is.data.frame(transcript_data)) {
|
| 119 | 1x |
stop("transcript_data must be a tibble or data frame")
|
| 120 |
} |
|
| 121 | ||
| 122 |
# Apply privacy protection |
|
| 123 | 4x |
export_data <- zoomstudentengagement::ensure_privacy( |
| 124 | 4x |
transcript_data, |
| 125 | 4x |
privacy_level = privacy_level |
| 126 |
) |
|
| 127 | ||
| 128 |
# Convert to list structure |
|
| 129 | 4x |
json_data <- list( |
| 130 | 4x |
transcript_data = as.list(export_data), |
| 131 | 4x |
export_info = list( |
| 132 | 4x |
timestamp = Sys.time(), |
| 133 | 4x |
format = "json", |
| 134 | 4x |
privacy_level = privacy_level, |
| 135 | 4x |
row_count = nrow(export_data), |
| 136 | 4x |
column_count = ncol(export_data) |
| 137 |
) |
|
| 138 |
) |
|
| 139 | ||
| 140 |
# Add metadata if requested |
|
| 141 | 4x |
if (include_metadata) {
|
| 142 | 4x |
json_data$metadata <- generate_export_metadata(export_data, format = "json") |
| 143 |
} |
|
| 144 | ||
| 145 |
# Determine output path |
|
| 146 | 4x |
if (is.null(file_path)) {
|
| 147 | 1x |
timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S") |
| 148 | 1x |
file_path <- paste0("ideal_transcript_export_", timestamp, ".json")
|
| 149 |
} |
|
| 150 | ||
| 151 |
# Create directory if needed |
|
| 152 | 4x |
dir_path <- dirname(file_path) |
| 153 | 4x |
if (dir_path != "." && !dir.exists(dir_path)) {
|
| 154 | ! |
dir.create(dir_path, recursive = TRUE) |
| 155 |
} |
|
| 156 | ||
| 157 |
# Write JSON file |
|
| 158 | 4x |
jsonlite::write_json( |
| 159 | 4x |
json_data, |
| 160 | 4x |
file_path, |
| 161 | 4x |
pretty = pretty_print, |
| 162 | 4x |
auto_unbox = TRUE |
| 163 |
) |
|
| 164 | ||
| 165 | 4x |
invisible(file_path) |
| 166 |
} |
|
| 167 | ||
| 168 |
#' Export Ideal Course Transcripts to Excel (Temporarily CSV) |
|
| 169 |
#' |
|
| 170 |
#' Exports ideal course transcript data to CSV format as a temporary workaround |
|
| 171 |
#' for Excel export segfault issues. This function temporarily creates CSV files |
|
| 172 |
#' instead of Excel files due to openxlsx package segfault problems. |
|
| 173 |
#' |
|
| 174 |
#' @param transcript_data Data frame containing transcript data |
|
| 175 |
#' @param file_path Character. Output file path. If NULL, generates default name |
|
| 176 |
#' @param privacy_level Character. Privacy level for data masking. Default from option |
|
| 177 |
#' @param include_summary_sheet Logical. Whether to include summary sheet. Default: TRUE |
|
| 178 |
#' @param include_metadata_sheet Logical. Whether to include metadata sheet. Default: TRUE |
|
| 179 |
#' @return Invisibly returns the file path (CSV format due to temporary workaround) |
|
| 180 |
#' @export |
|
| 181 |
#' @keywords deprecated |
|
| 182 |
#' @examples |
|
| 183 |
#' \dontrun{
|
|
| 184 |
#' # Export with default settings (creates CSV file) |
|
| 185 |
#' export_ideal_transcripts_excel(transcript_data) |
|
| 186 |
#' |
|
| 187 |
#' # Export with custom sheets (creates separate CSV files) |
|
| 188 |
#' export_ideal_transcripts_excel( |
|
| 189 |
#' transcript_data, |
|
| 190 |
#' include_summary_sheet = FALSE, |
|
| 191 |
#' include_metadata_sheet = TRUE |
|
| 192 |
#' ) |
|
| 193 |
#' } |
|
| 194 |
#' @note |
|
| 195 |
#' This function temporarily exports to CSV format instead of Excel due to |
|
| 196 |
#' segfault issues with the openxlsx package. The function name is maintained |
|
| 197 |
#' for backward compatibility, but it creates CSV files with appropriate |
|
| 198 |
#' extensions. Summary and metadata sheets are created as separate CSV files |
|
| 199 |
#' when requested. |
|
| 200 |
export_ideal_transcripts_excel <- function( |
|
| 201 |
transcript_data = NULL, |
|
| 202 |
file_path = NULL, |
|
| 203 |
privacy_level = getOption("zoomstudentengagement.privacy_level", "mask"),
|
|
| 204 |
include_summary_sheet = TRUE, |
|
| 205 |
include_metadata_sheet = TRUE) {
|
|
| 206 |
# DEPRECATED: This function will be removed in the next version |
|
| 207 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 208 | 6x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 209 | ! |
warning("Function 'export_ideal_transcripts_excel' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 210 |
} |
|
| 211 | ||
| 212 |
# Validate inputs |
|
| 213 | 6x |
if (is.null(transcript_data)) {
|
| 214 | 1x |
stop("transcript_data cannot be NULL")
|
| 215 |
} |
|
| 216 | ||
| 217 | 5x |
if (!tibble::is_tibble(transcript_data) && !is.data.frame(transcript_data)) {
|
| 218 | 1x |
stop("transcript_data must be a tibble or data frame")
|
| 219 |
} |
|
| 220 | ||
| 221 |
# Apply privacy protection |
|
| 222 | 4x |
export_data <- zoomstudentengagement::ensure_privacy( |
| 223 | 4x |
transcript_data, |
| 224 | 4x |
privacy_level = privacy_level |
| 225 |
) |
|
| 226 | ||
| 227 |
# Convert to data frame and ensure all columns are simple types |
|
| 228 | 4x |
export_data <- as.data.frame(export_data, stringsAsFactors = FALSE) |
| 229 | ||
| 230 |
# Convert list columns to character strings |
|
| 231 | 4x |
for (col in names(export_data)) {
|
| 232 | 16x |
if (is.list(export_data[[col]])) {
|
| 233 | ! |
export_data[[col]] <- sapply(export_data[[col]], function(x) {
|
| 234 | ! |
if (is.null(x)) {
|
| 235 | ! |
return(NA_character_) |
| 236 |
} |
|
| 237 | ! |
if (length(x) == 0) {
|
| 238 | ! |
return(NA_character_) |
| 239 |
} |
|
| 240 | ! |
paste(as.character(x), collapse = "; ") |
| 241 |
}) |
|
| 242 |
} |
|
| 243 |
} |
|
| 244 | ||
| 245 |
# Ensure all columns are atomic types that openxlsx can handle |
|
| 246 | 4x |
for (col in names(export_data)) {
|
| 247 | 16x |
if (!is.atomic(export_data[[col]])) {
|
| 248 | ! |
export_data[[col]] <- as.character(export_data[[col]]) |
| 249 |
} |
|
| 250 |
} |
|
| 251 | ||
| 252 |
# Convert any remaining complex types to character |
|
| 253 | 4x |
export_data <- data.frame( |
| 254 | 4x |
lapply(export_data, function(x) {
|
| 255 | 16x |
if (is.factor(x)) {
|
| 256 | ! |
as.character(x) |
| 257 | 16x |
} else if (is.list(x)) {
|
| 258 | ! |
sapply(x, function(y) if (is.null(y)) NA_character_ else as.character(y)) |
| 259 |
} else {
|
|
| 260 | 16x |
x |
| 261 |
} |
|
| 262 |
}), |
|
| 263 | 4x |
stringsAsFactors = FALSE |
| 264 |
) |
|
| 265 | ||
| 266 |
# Determine output path first |
|
| 267 | 4x |
if (is.null(file_path)) {
|
| 268 | 1x |
timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S") |
| 269 | 1x |
file_path <- paste0("ideal_transcript_export_", timestamp, ".xlsx")
|
| 270 |
} |
|
| 271 | ||
| 272 |
# Ensure file_path is not empty |
|
| 273 | 4x |
if (file_path == "" || is.na(file_path)) {
|
| 274 | ! |
timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S") |
| 275 | ! |
file_path <- paste0("ideal_transcript_export_", timestamp, ".xlsx")
|
| 276 |
} |
|
| 277 | ||
| 278 |
# Create directory if needed |
|
| 279 | 4x |
dir_path <- dirname(file_path) |
| 280 | 4x |
if (dir_path != "." && !dir.exists(dir_path)) {
|
| 281 | ! |
dir.create(dir_path, recursive = TRUE) |
| 282 |
} |
|
| 283 | ||
| 284 |
# TEMPORARILY DISABLED: Excel workbook creation due to segfault issues |
|
| 285 |
# wb <- openxlsx::createWorkbook() |
|
| 286 |
# openxlsx::addWorksheet(wb, "Transcript Data") |
|
| 287 | ||
| 288 |
# TEMPORARY WORKAROUND: Skip Excel export due to segfault issues |
|
| 289 |
# Create a CSV file instead of Excel to avoid openxlsx segfault |
|
| 290 | 4x |
warning("Excel export temporarily disabled due to segfault issues. Creating CSV file instead.")
|
| 291 | ||
| 292 |
# Create CSV file as alternative |
|
| 293 | 4x |
csv_file <- gsub("\\.xlsx$", ".csv", file_path)
|
| 294 | 4x |
utils::write.csv(export_data, csv_file, row.names = FALSE) |
| 295 | ||
| 296 |
# Create a simple text file explaining the situation |
|
| 297 | 4x |
info_file <- gsub("\\.xlsx$", "_info.txt", file_path)
|
| 298 | 4x |
info_content <- c( |
| 299 | 4x |
"Excel export temporarily disabled due to segfault issues.", |
| 300 | 4x |
"Data has been exported to CSV format instead.", |
| 301 | 4x |
paste("CSV file:", csv_file),
|
| 302 | 4x |
paste("Timestamp:", Sys.time()),
|
| 303 |
"", |
|
| 304 | 4x |
"To re-enable Excel export, the openxlsx package segfault issue must be resolved." |
| 305 |
) |
|
| 306 | 4x |
writeLines(info_content, info_file) |
| 307 | ||
| 308 |
# Return the CSV file path instead of Excel file path |
|
| 309 |
# Note: This function now returns a CSV file due to Excel segfault issues |
|
| 310 |
# But we need to maintain the original file_path for backward compatibility |
|
| 311 |
# The actual CSV file is created at csv_file, but we return the original path |
|
| 312 |
# This allows tests to still expect .xlsx files while we create .csv files |
|
| 313 | ||
| 314 |
# Add summary sheet if requested (temporarily disabled due to segfault) |
|
| 315 | 4x |
if (include_summary_sheet) {
|
| 316 | 3x |
warning("Summary sheet temporarily disabled due to Excel segfault issues")
|
| 317 |
# Create summary as separate CSV file |
|
| 318 | 3x |
summary_data <- generate_transcript_summary(export_data) |
| 319 | 3x |
if (is.data.frame(summary_data)) {
|
| 320 | 3x |
summary_csv_file <- gsub("\\.xlsx$", "_summary.csv", file_path)
|
| 321 | 3x |
utils::write.csv(summary_data, summary_csv_file, row.names = FALSE) |
| 322 |
} |
|
| 323 |
} |
|
| 324 | ||
| 325 |
# Add metadata sheet if requested (temporarily disabled due to segfault) |
|
| 326 | 4x |
if (include_metadata_sheet) {
|
| 327 | 3x |
warning("Metadata sheet temporarily disabled due to Excel segfault issues")
|
| 328 |
# Create metadata as separate CSV file |
|
| 329 | 3x |
metadata_data <- generate_export_metadata(export_data, format = "excel") |
| 330 | 3x |
if (is.data.frame(metadata_data)) {
|
| 331 | ! |
metadata_csv_file <- gsub("\\.xlsx$", "_metadata.csv", file_path)
|
| 332 | ! |
utils::write.csv(metadata_data, metadata_csv_file, row.names = FALSE) |
| 333 |
} |
|
| 334 |
} |
|
| 335 | ||
| 336 | ||
| 337 | ||
| 338 |
# TEMPORARILY DISABLED: Excel workbook saving due to segfault issues |
|
| 339 |
# openxlsx::saveWorkbook(wb, file_path, overwrite = TRUE) |
|
| 340 | ||
| 341 |
# Return the actual CSV file path since we're creating CSV instead of Excel |
|
| 342 | 4x |
invisible(csv_file) |
| 343 |
} |
|
| 344 | ||
| 345 |
#' Export Ideal Course Transcripts Summary Report |
|
| 346 |
#' |
|
| 347 |
#' Creates and exports summary reports for ideal course transcripts in multiple |
|
| 348 |
#' formats with key metrics and insights. Note: Excel export temporarily creates |
|
| 349 |
#' CSV files due to openxlsx segfault issues. |
|
| 350 |
#' |
|
| 351 |
#' @param transcript_data Data frame containing transcript data |
|
| 352 |
#' @param file_path Character. Output file path. If NULL, generates default name |
|
| 353 |
#' @param format Character. Output format: "csv", "json", or "excel". Default: "csv" |
|
| 354 |
#' @param privacy_level Character. Privacy level for data masking. Default from option |
|
| 355 |
#' @param include_charts Logical. Whether to include charts (Excel only). Default: FALSE |
|
| 356 |
#' @return Invisibly returns the summary data |
|
| 357 |
#' @export |
|
| 358 |
#' @keywords deprecated |
|
| 359 |
#' @examples |
|
| 360 |
#' \dontrun{
|
|
| 361 |
#' # Export summary as CSV |
|
| 362 |
#' export_ideal_transcripts_summary(transcript_data, format = "csv") |
|
| 363 |
#' |
|
| 364 |
#' # Export summary as Excel with charts |
|
| 365 |
#' export_ideal_transcripts_summary( |
|
| 366 |
#' transcript_data, |
|
| 367 |
#' format = "excel", |
|
| 368 |
#' include_charts = TRUE |
|
| 369 |
#' ) |
|
| 370 |
#' } |
|
| 371 |
export_ideal_transcripts_summary <- function( |
|
| 372 |
transcript_data = NULL, |
|
| 373 |
file_path = NULL, |
|
| 374 |
format = c("csv", "json", "excel"),
|
|
| 375 |
privacy_level = getOption("zoomstudentengagement.privacy_level", "mask"),
|
|
| 376 |
include_charts = FALSE) {
|
|
| 377 |
# DEPRECATED: This function will be removed in the next version |
|
| 378 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 379 | 7x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 380 | ! |
warning("Function 'export_ideal_transcripts_summary' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 381 |
} |
|
| 382 | ||
| 383 |
# Validate inputs |
|
| 384 | 7x |
if (is.null(transcript_data)) {
|
| 385 | 1x |
stop("transcript_data cannot be NULL")
|
| 386 |
} |
|
| 387 | ||
| 388 | 6x |
if (!tibble::is_tibble(transcript_data) && !is.data.frame(transcript_data)) {
|
| 389 | 1x |
stop("transcript_data must be a tibble or data frame")
|
| 390 |
} |
|
| 391 | ||
| 392 | 5x |
format <- match.arg(format) |
| 393 | ||
| 394 |
# Apply privacy protection |
|
| 395 | 5x |
export_data <- zoomstudentengagement::ensure_privacy( |
| 396 | 5x |
transcript_data, |
| 397 | 5x |
privacy_level = privacy_level |
| 398 |
) |
|
| 399 | ||
| 400 |
# Generate summary data |
|
| 401 | 5x |
summary_data <- generate_transcript_summary(export_data) |
| 402 | ||
| 403 |
# Determine output path |
|
| 404 | 5x |
if (is.null(file_path)) {
|
| 405 | 1x |
timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S") |
| 406 | 1x |
file_path <- paste0("ideal_transcript_summary_", timestamp, ".", format)
|
| 407 |
} |
|
| 408 | ||
| 409 |
# Export based on format |
|
| 410 | 5x |
if (format == "csv") {
|
| 411 | 3x |
utils::write.csv(summary_data, file_path, row.names = FALSE) |
| 412 | 2x |
} else if (format == "json") {
|
| 413 | 1x |
json_data <- list( |
| 414 | 1x |
summary_data = as.list(summary_data), |
| 415 | 1x |
export_info = list( |
| 416 | 1x |
timestamp = Sys.time(), |
| 417 | 1x |
format = "json", |
| 418 | 1x |
privacy_level = privacy_level |
| 419 |
) |
|
| 420 |
) |
|
| 421 | 1x |
jsonlite::write_json(json_data, file_path, pretty = TRUE, auto_unbox = TRUE) |
| 422 | 1x |
} else if (format == "excel") {
|
| 423 |
# TEMPORARY WORKAROUND: Skip Excel export due to segfault issues |
|
| 424 |
# Create a CSV file instead of Excel to avoid openxlsx segfault |
|
| 425 | 1x |
warning("Excel export temporarily disabled due to segfault issues. Creating CSV file instead.")
|
| 426 | ||
| 427 |
# Create CSV file as alternative |
|
| 428 | 1x |
csv_file <- gsub("\\.xlsx$", ".csv", file_path)
|
| 429 | 1x |
utils::write.csv(summary_data, csv_file, row.names = FALSE) |
| 430 | ||
| 431 |
# Create a simple text file explaining the situation |
|
| 432 | 1x |
info_file <- gsub("\\.xlsx$", "_info.txt", file_path)
|
| 433 | 1x |
info_content <- c( |
| 434 | 1x |
"Excel export temporarily disabled due to segfault issues.", |
| 435 | 1x |
"Summary data has been exported to CSV format instead.", |
| 436 | 1x |
paste("CSV file:", csv_file),
|
| 437 | 1x |
paste("Timestamp:", Sys.time()),
|
| 438 |
"", |
|
| 439 | 1x |
"To re-enable Excel export, the openxlsx package segfault issue must be resolved." |
| 440 |
) |
|
| 441 | 1x |
writeLines(info_content, info_file) |
| 442 | ||
| 443 |
# Return the CSV file path instead of Excel file path |
|
| 444 | 1x |
file_path <- csv_file |
| 445 |
} |
|
| 446 | ||
| 447 | 5x |
invisible(file_path) |
| 448 |
} |
|
| 449 | ||
| 450 |
#' Add Export Metadata |
|
| 451 |
#' @keywords internal |
|
| 452 |
add_export_metadata <- function(data, format = "csv") {
|
|
| 453 |
# DEPRECATED: This function will be removed in the next version |
|
| 454 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 455 | 13x |
warning("Function 'add_export_metadata' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 456 | ||
| 457 |
# Add export timestamp and format info |
|
| 458 | 13x |
data$export_timestamp <- Sys.time() |
| 459 | 13x |
data$export_format <- format |
| 460 | 13x |
data$export_version <- "1.0.0" |
| 461 | 13x |
return(data) |
| 462 |
} |
|
| 463 | ||
| 464 |
#' Generate Export Metadata |
|
| 465 |
#' @keywords internal |
|
| 466 |
generate_export_metadata <- function(data, format = "csv") {
|
|
| 467 |
# DEPRECATED: This function will be removed in the next version |
|
| 468 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 469 | 7x |
warning("Function 'generate_export_metadata' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 470 | ||
| 471 | 7x |
metadata <- list( |
| 472 | 7x |
export_timestamp = Sys.time(), |
| 473 | 7x |
export_format = format, |
| 474 | 7x |
export_version = "1.0.0", |
| 475 | 7x |
row_count = nrow(data), |
| 476 | 7x |
column_count = ncol(data), |
| 477 | 7x |
column_names = names(data), |
| 478 | 7x |
data_types = sapply(data, class) |
| 479 |
) |
|
| 480 | 7x |
return(metadata) |
| 481 |
} |
|
| 482 | ||
| 483 |
#' Generate Transcript Summary |
|
| 484 |
#' @keywords internal |
|
| 485 |
generate_transcript_summary <- function(data) {
|
|
| 486 |
# DEPRECATED: This function will be removed in the next version |
|
| 487 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 488 | 8x |
warning("Function 'generate_transcript_summary' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 489 | ||
| 490 | 8x |
summary_data <- list( |
| 491 | 8x |
total_rows = nrow(data), |
| 492 | 8x |
total_columns = ncol(data), |
| 493 | 8x |
unique_speakers = if ("name" %in% names(data)) length(unique(data$name)) else NA,
|
| 494 | 8x |
time_range = if (all(c("start", "end") %in% names(data))) {
|
| 495 | 8x |
paste(min(data$start), "to", max(data$end)) |
| 496 |
} else {
|
|
| 497 | 8x |
NA |
| 498 |
}, |
|
| 499 | 8x |
export_timestamp = Sys.time() |
| 500 |
) |
|
| 501 | ||
| 502 | 8x |
return(as.data.frame(summary_data)) |
| 503 |
} |
|
| 504 | ||
| 505 |
#' Add Summary Charts to Excel Workbook |
|
| 506 |
#' @keywords internal |
|
| 507 |
add_summary_charts <- function(wb, summary_data) {
|
|
| 508 |
# DEPRECATED: This function will be removed in the next version |
|
| 509 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 510 | ! |
warning("Function 'add_summary_charts' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 511 | ||
| 512 |
# Add basic charts if summary data is available |
|
| 513 |
# This is a placeholder for future chart functionality |
|
| 514 |
# Chart functionality not yet implemented |
|
| 515 |
} |
| 1 |
#' Summarize Transcript Metrics |
|
| 2 |
#' |
|
| 3 |
#' Process a Zoom recording transcript and return summary metrics by speaker |
|
| 4 |
#' |
|
| 5 |
#' Original code posted by Conor Healy: |
|
| 6 |
#' https://ucbischool.slack.com/archives/C02A36407K9/p1631855705002000 Addition |
|
| 7 |
#' of `wordcount`, `wordcount_perc`, and `wpm` by Brooks Ambrose: |
|
| 8 |
#' https://gist.github.com/brooksambrose/1a8a673eb3bf884c1868ad4d80f08246 |
|
| 9 | ||
| 10 | ||
| 11 | ||
| 12 | ||
| 13 |
#' @param transcript_file_path File path of a .transcript.vtt file of a Zoom recording |
|
| 14 |
#' transcript. |
|
| 15 |
#' @param names_exclude Character vector of names to exclude from the results. |
|
| 16 |
#' Defaults to 'c("dead_air")'
|
|
| 17 |
#' @param consolidate_comments Set to `TRUE` to consolidate consecutive comments |
|
| 18 |
#' from the same speaker with gaps of less than `max_pause_sec`. `FALSE` |
|
| 19 |
#' returns the results from the raw transcript. Defaults to `TRUE` |
|
| 20 |
#' @param max_pause_sec Maximum pause between comments to be consolidated. If |
|
| 21 |
#' the raw comments from the Zoom recording transcript contain 2 consecutive |
|
| 22 |
#' comments from the same speaker, and the time between the end of the first |
|
| 23 |
#' comment and start of the second comment is less than `max_pause_sec` |
|
| 24 |
#' seconds, then the comments will be consolidated. If the time between the |
|
| 25 |
#' comments is larger, they will not be consolidated. Defaults to 1. |
|
| 26 |
#' @param add_dead_air Set to `TRUE` to adds rows for any time between |
|
| 27 |
#' transcribed comments, labeled with the `dead_air_name` provided (or the |
|
| 28 |
#' default value of 'dead_air'). The resulting tibble will have rows |
|
| 29 |
#' accounting for the time from the beginning of the first comment to the end |
|
| 30 |
#' of the last one. Defaults to `TRUE`. |
|
| 31 |
#' @param dead_air_name Character string to label the `name` column in any rows |
|
| 32 |
#' added for dead air. Defaults to 'dead_air'. |
|
| 33 |
#' @param na_name Character string to label the `name` column in any rows where |
|
| 34 |
#' the transcript `name` is `NA`. Defaults to 'unknown'. |
|
| 35 |
#' @param transcript_df Tibble containing the comments from a Zoom recording transcript (which is generally the result of calling `process_zoom_transcript()`. |
|
| 36 |
#' @param comments_format Character string specifying how to format the comments column. |
|
| 37 |
#' Options: "list" (default, preserves list structure), "text" (semicolon-separated text), |
|
| 38 |
#' or "count" (just the number of comments). Defaults to "list". |
|
| 39 |
#' |
|
| 40 |
#' @return A tibble containing summary metrics by speaker from a Zoom recording |
|
| 41 |
#' transcript |
|
| 42 |
#' @export |
|
| 43 |
#' |
|
| 44 |
#' @examples |
|
| 45 |
#' # Load a sample transcript from the package's extdata directory |
|
| 46 |
#' transcript_file <- system.file("extdata/transcripts/GMT20240124-202901_Recording.transcript.vtt",
|
|
| 47 |
#' package = "zoomstudentengagement" |
|
| 48 |
#' ) |
|
| 49 |
#' summarize_transcript_metrics(transcript_file_path = transcript_file) |
|
| 50 |
#' |
|
| 51 |
summarize_transcript_metrics <- function(transcript_file_path = "", |
|
| 52 |
names_exclude = c("dead_air"),
|
|
| 53 |
consolidate_comments = TRUE, |
|
| 54 |
max_pause_sec = 1, |
|
| 55 |
add_dead_air = TRUE, |
|
| 56 |
dead_air_name = "dead_air", |
|
| 57 |
na_name = "unknown", |
|
| 58 |
transcript_df = NULL, |
|
| 59 |
comments_format = c("list", "text", "count")) {
|
|
| 60 |
. <- |
|
| 61 | 181x |
begin <- |
| 62 | 181x |
comment_num <- |
| 63 | 181x |
duration <- |
| 64 | 181x |
end <- |
| 65 | 181x |
n <- |
| 66 | 181x |
name <- prior_dead_air <- start <- timestamp <- wordcount <- transcript_file <- NULL |
| 67 | ||
| 68 | 181x |
consolidate_comments_ <- consolidate_comments |
| 69 | 181x |
max_pause_sec_ <- max_pause_sec |
| 70 | 181x |
add_dead_air_ <- add_dead_air |
| 71 | 181x |
dead_air_name_ <- dead_air_name |
| 72 | 181x |
na_name_ <- na_name |
| 73 | 181x |
comments_format <- match.arg(comments_format) |
| 74 | ||
| 75 | ||
| 76 | 181x |
if (file.exists(transcript_file_path)) {
|
| 77 | 167x |
transcript_df <- zoomstudentengagement::process_zoom_transcript( |
| 78 | 167x |
transcript_file_path, |
| 79 | 167x |
consolidate_comments = consolidate_comments_, |
| 80 | 167x |
max_pause_sec = max_pause_sec_, |
| 81 | 167x |
add_dead_air = add_dead_air_, |
| 82 | 167x |
dead_air_name = dead_air_name_, |
| 83 | 167x |
na_name = na_name_ |
| 84 |
) |
|
| 85 |
} |
|
| 86 | ||
| 87 | ||
| 88 | 181x |
if (tibble::is_tibble(transcript_df)) {
|
| 89 |
# Check if transcript_file column exists and prepare grouping |
|
| 90 | 178x |
group_vars <- c("name")
|
| 91 | 178x |
if ("transcript_file" %in% names(transcript_df)) {
|
| 92 | 167x |
group_vars <- c("transcript_file", "name")
|
| 93 |
} |
|
| 94 | ||
| 95 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 96 |
# Filter out excluded names |
|
| 97 | 178x |
filtered_df <- transcript_df[!transcript_df$name %in% unlist(names_exclude), , drop = FALSE] |
| 98 | ||
| 99 | 178x |
if (nrow(filtered_df) == 0) {
|
| 100 | 4x |
return(tibble::tibble( |
| 101 | 4x |
name = character(), |
| 102 | 4x |
n = numeric(), |
| 103 | 4x |
duration = numeric(), |
| 104 | 4x |
wordcount = numeric(), |
| 105 | 4x |
comments = list(), |
| 106 |
# Canonical percentage columns |
|
| 107 | 4x |
perc_n = numeric(), |
| 108 | 4x |
perc_duration = numeric(), |
| 109 | 4x |
perc_wordcount = numeric(), |
| 110 |
# Temporary aliases for backward compatibility (to be deprecated) |
|
| 111 | 4x |
n_perc = numeric(), |
| 112 | 4x |
duration_perc = numeric(), |
| 113 | 4x |
wordcount_perc = numeric(), |
| 114 | 4x |
wpm = numeric() |
| 115 |
)) |
|
| 116 |
} |
|
| 117 | ||
| 118 |
# Create a unique identifier for each group |
|
| 119 | 174x |
filtered_df$group_id <- apply(filtered_df[, group_vars], 1, paste, collapse = "|") |
| 120 | ||
| 121 |
# Aggregate by group using base R |
|
| 122 | 174x |
group_ids <- unique(filtered_df$group_id) |
| 123 | 174x |
result_rows <- list() |
| 124 | ||
| 125 | 174x |
for (i in seq_along(group_ids)) {
|
| 126 | 1146x |
group_id <- group_ids[i] |
| 127 | 1146x |
group_data <- filtered_df[filtered_df$group_id == group_id, , drop = FALSE] |
| 128 | ||
| 129 |
# Calculate summaries |
|
| 130 | 1146x |
n_count <- nrow(group_data) |
| 131 | 1146x |
duration_sum <- sum(as.numeric(group_data$duration), na.rm = TRUE) |
| 132 | 1146x |
wordcount_sum <- sum(as.numeric(group_data$wordcount), na.rm = TRUE) |
| 133 | ||
| 134 |
# Format comments based on comments_format parameter |
|
| 135 | 1146x |
raw_comments <- group_data$comment |
| 136 | 1146x |
if (comments_format == "text") {
|
| 137 | 2x |
comments_col <- paste(unlist(raw_comments), collapse = "; ") |
| 138 | 1144x |
} else if (comments_format == "count") {
|
| 139 | 2x |
comments_col <- length(unlist(raw_comments)) |
| 140 |
} else {
|
|
| 141 |
# "list" format (default) |
|
| 142 | 1142x |
comments_col <- list(raw_comments) |
| 143 |
} |
|
| 144 | ||
| 145 |
# Get group identifiers |
|
| 146 | 1146x |
group_parts <- strsplit(group_id, "\\|")[[1]] |
| 147 | ||
| 148 |
# Create result row |
|
| 149 | 1146x |
if (length(group_vars) == 1) {
|
| 150 | 13x |
result_row <- data.frame( |
| 151 | 13x |
name = group_parts[1], |
| 152 | 13x |
n = n_count, |
| 153 | 13x |
duration = duration_sum, |
| 154 | 13x |
wordcount = wordcount_sum, |
| 155 | 13x |
comments = I(comments_col), |
| 156 | 13x |
stringsAsFactors = FALSE |
| 157 |
) |
|
| 158 |
} else {
|
|
| 159 | 1133x |
result_row <- data.frame( |
| 160 | 1133x |
transcript_file = group_parts[1], |
| 161 | 1133x |
name = group_parts[2], |
| 162 | 1133x |
n = n_count, |
| 163 | 1133x |
duration = duration_sum, |
| 164 | 1133x |
wordcount = wordcount_sum, |
| 165 | 1133x |
comments = I(comments_col), |
| 166 | 1133x |
stringsAsFactors = FALSE |
| 167 |
) |
|
| 168 |
} |
|
| 169 | ||
| 170 | 1146x |
result_rows[[i]] <- result_row |
| 171 |
} |
|
| 172 | ||
| 173 |
# Combine results |
|
| 174 | 174x |
result <- do.call(rbind, result_rows) |
| 175 | ||
| 176 |
# Calculate percentages using base R |
|
| 177 | 174x |
total_n <- sum(result$n, na.rm = TRUE) |
| 178 | 174x |
total_duration <- sum(result$duration, na.rm = TRUE) |
| 179 | 174x |
total_wordcount <- sum(result$wordcount, na.rm = TRUE) |
| 180 | ||
| 181 |
# Canonical percentage columns (preferred naming) |
|
| 182 | 174x |
result$perc_n <- result$n / total_n * 100 |
| 183 | 174x |
result$perc_duration <- result$duration / total_duration * 100 |
| 184 | 174x |
result$perc_wordcount <- result$wordcount / total_wordcount * 100 |
| 185 |
# Temporary aliases for backward compatibility (to be deprecated) |
|
| 186 | 174x |
result$n_perc <- result$perc_n |
| 187 | 174x |
result$duration_perc <- result$perc_duration |
| 188 | 174x |
result$wordcount_perc <- result$perc_wordcount |
| 189 | 174x |
result$wpm <- result$wordcount / result$duration |
| 190 | ||
| 191 |
# Sort by duration (descending) using base R |
|
| 192 | 174x |
result <- result[order(-result$duration), , drop = FALSE] |
| 193 | ||
| 194 |
# Convert to tibble to maintain expected return type |
|
| 195 | 174x |
result <- tibble::as_tibble(result) |
| 196 | ||
| 197 |
# Attach provenance attributes |
|
| 198 | 174x |
attr(result, "schema_version") <- "1.0" |
| 199 | 174x |
attr(result, "source_files") <- if (!is.null(transcript_file_path) && nzchar(transcript_file_path)) basename(transcript_file_path) else NA_character_ |
| 200 | 174x |
attr(result, "processing_timestamp") <- as.character(Sys.time()) |
| 201 | 174x |
attr(result, "privacy_level") <- getOption("zoomstudentengagement.privacy_level", "mask")
|
| 202 | ||
| 203 |
# Apply privacy before returning |
|
| 204 | 174x |
return(zoomstudentengagement::ensure_privacy(result)) |
| 205 |
} |
|
| 206 |
} |
| 1 |
#' Make Transcripts Session Summary |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from the provided tibble containing session |
|
| 4 |
#' details and summary metrics by speaker for all class sessions (and |
|
| 5 |
#' placeholders for missing sections), including customized student names, and |
|
| 6 |
#' summarizes results at the level of the session and preferred student name. |
|
| 7 |
#' |
|
| 8 |
#' @param clean_names_df A tibble containing session details and summary metrics |
|
| 9 |
#' by speaker for all class sessions (and placeholders for missing sections), |
|
| 10 |
#' including customized student names. |
|
| 11 |
#' |
|
| 12 |
#' @return a tibble containing session details and |
|
| 13 |
#' summary metrics by speaker for all class sessions (and placeholders for |
|
| 14 |
#' missing sections), including customized student names, and summarizes |
|
| 15 |
#' results at the level of the session and preferred student name. |
|
| 16 |
#' @export |
|
| 17 |
#' @keywords deprecated |
|
| 18 |
#' |
|
| 19 |
#' @examples |
|
| 20 |
#' # Load required packages |
|
| 21 |
#' library(dplyr) |
|
| 22 |
#' |
|
| 23 |
#' # Create a simple sample data frame for testing |
|
| 24 |
#' sample_data <- tibble::tibble( |
|
| 25 |
#' section = c("A", "A", "B"),
|
|
| 26 |
#' preferred_name = c("John Smith", "Jane Doe", "Bob Wilson"),
|
|
| 27 |
#' n = c(5, 3, 2), |
|
| 28 |
#' duration = c(300, 180, 120), |
|
| 29 |
#' wordcount = c(500, 300, 200) |
|
| 30 |
#' ) |
|
| 31 |
#' |
|
| 32 |
#' # Test the function with the sample data |
|
| 33 |
#' make_transcripts_session_summary_df(sample_data) |
|
| 34 |
make_transcripts_session_summary_df <- function(clean_names_df = NULL) {
|
|
| 35 |
# DEPRECATED: This function will be removed in the next version |
|
| 36 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 37 | 11x |
warning("Function 'make_transcripts_session_summary_df' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 38 | ||
| 39 | 11x |
if (is.null(clean_names_df)) {
|
| 40 | 1x |
return(NULL) |
| 41 |
} |
|
| 42 | 10x |
if (!tibble::is_tibble(clean_names_df)) {
|
| 43 | 1x |
stop("clean_names_df must be a tibble or NULL.")
|
| 44 |
} |
|
| 45 | ||
| 46 |
# Check for empty input |
|
| 47 | 9x |
if (nrow(clean_names_df) == 0) {
|
| 48 | 2x |
return(tibble::tibble( |
| 49 | 2x |
section = character(), |
| 50 | 2x |
day = character(), |
| 51 | 2x |
time = character(), |
| 52 | 2x |
session_num = integer(), |
| 53 | 2x |
preferred_name = character(), |
| 54 | 2x |
course_section = character(), |
| 55 | 2x |
wordcount = numeric(), |
| 56 | 2x |
duration = numeric(), |
| 57 | 2x |
n = integer(), |
| 58 | 2x |
n_perc = numeric(), |
| 59 | 2x |
duration_perc = numeric(), |
| 60 | 2x |
wordcount_perc = numeric(), |
| 61 | 2x |
wpm = numeric() |
| 62 |
)) |
|
| 63 |
} |
|
| 64 | ||
| 65 |
# Define expected columns |
|
| 66 | 7x |
expected_cols <- c("section", "day", "time", "session_num", "preferred_name", "course_section", "wordcount", "duration")
|
| 67 |
# Filter to only use columns that are present |
|
| 68 | 7x |
available_cols <- intersect(expected_cols, names(clean_names_df)) |
| 69 | 7x |
if (length(available_cols) == 0) {
|
| 70 | 1x |
stop("clean_names_df must contain at least one of the expected columns: section, day, time, session_num, preferred_name, course_section, wordcount, duration.")
|
| 71 |
} |
|
| 72 | ||
| 73 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 74 |
# Create a unique identifier for each group |
|
| 75 | 6x |
clean_names_df$group_id <- apply(clean_names_df[, available_cols], 1, paste, collapse = "|") |
| 76 | ||
| 77 |
# Aggregate by group using base R |
|
| 78 | 6x |
group_ids <- unique(clean_names_df$group_id) |
| 79 | 6x |
result_rows <- list() |
| 80 | ||
| 81 | 6x |
for (i in seq_along(group_ids)) {
|
| 82 | 17x |
group_id <- group_ids[i] |
| 83 | 17x |
group_data <- clean_names_df[clean_names_df$group_id == group_id, , drop = FALSE] |
| 84 | ||
| 85 |
# Calculate summaries |
|
| 86 | 17x |
n_count <- nrow(group_data) |
| 87 | 17x |
duration_sum <- sum(group_data$duration, na.rm = TRUE) |
| 88 | 17x |
wordcount_sum <- sum(group_data$wordcount, na.rm = TRUE) |
| 89 | ||
| 90 |
# Get group identifiers |
|
| 91 | 17x |
group_parts <- strsplit(group_id, "\\|")[[1]] |
| 92 | ||
| 93 |
# Create result row with all available columns |
|
| 94 | 17x |
result_row <- list() |
| 95 | 17x |
for (j in seq_along(available_cols)) {
|
| 96 | 111x |
result_row[[available_cols[j]]] <- group_parts[j] |
| 97 |
} |
|
| 98 | ||
| 99 |
# Add summary columns |
|
| 100 | 17x |
result_row$n <- n_count |
| 101 | 17x |
result_row$duration <- duration_sum |
| 102 | 17x |
result_row$wordcount <- wordcount_sum |
| 103 | ||
| 104 | 17x |
result_rows[[i]] <- result_row |
| 105 |
} |
|
| 106 | ||
| 107 |
# Combine results |
|
| 108 | 6x |
result <- do.call(rbind, lapply(result_rows, function(x) {
|
| 109 | 17x |
as.data.frame(x, stringsAsFactors = FALSE) |
| 110 |
})) |
|
| 111 | ||
| 112 |
# Calculate percentages using base R |
|
| 113 | 6x |
total_n <- sum(result$n, na.rm = TRUE) |
| 114 | 6x |
total_duration <- sum(result$duration, na.rm = TRUE) |
| 115 | 6x |
total_wordcount <- sum(result$wordcount, na.rm = TRUE) |
| 116 | ||
| 117 | 6x |
result$n_perc <- result$n / total_n * 100 |
| 118 | 6x |
result$duration_perc <- result$duration / total_duration * 100 |
| 119 | 6x |
result$wordcount_perc <- result$wordcount / total_wordcount * 100 |
| 120 | 6x |
result$wpm <- result$wordcount / result$duration |
| 121 | ||
| 122 |
# Convert to tibble to maintain expected return type |
|
| 123 | 6x |
return(tibble::as_tibble(result)) |
| 124 |
} |
| 1 |
#' Make Cancelled Classes Tibble |
|
| 2 | ||
| 3 |
#' This function creates an empty tibble for recording of cancelled class |
|
| 4 |
#' sessions for scheduled classes where a Zoom recording is not expected. |
|
| 5 | ||
| 6 |
#' |
|
| 7 |
#' @return An empty tibble for recording of cancelled class sessions for scheduled classes |
|
| 8 |
#' where a zoom recording is not expected. |
|
| 9 |
#' @export |
|
| 10 |
#' @keywords deprecated |
|
| 11 |
#' |
|
| 12 |
#' @examples |
|
| 13 |
#' make_blank_cancelled_classes_df() |
|
| 14 |
make_blank_cancelled_classes_df <- function() {
|
|
| 15 |
# DEPRECATED: This function will be removed in the next version |
|
| 16 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 17 | 3x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 18 | ! |
warning("Function 'make_blank_cancelled_classes_df' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 19 |
} |
|
| 20 | ||
| 21 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 22 | 3x |
result <- readr::read_csv( |
| 23 | 3x |
I("dept,course_section,course,section,day,time,instructor,Topic,ID,Start Time,File Size (MB),File Count,Total Views,Total Downloads,Last Accessed,match_start_time,match_end_time,date_extract,recording_start,start_time_local,transcript_file,chat_file,closed_caption_file"),
|
| 24 | 3x |
col_types = "cciiccccccdiiicTTcTTccci" |
| 25 |
) |
|
| 26 | ||
| 27 |
# Apply transformations using base R instead of dplyr |
|
| 28 | 3x |
result$match_start_time <- as.POSIXct(result$match_start_time, tz = "America/Los_Angeles") |
| 29 | 3x |
result$match_end_time <- as.POSIXct(result$match_end_time, tz = "America/Los_Angeles") |
| 30 | 3x |
result$date_extract <- as.character(result$date_extract) |
| 31 | 3x |
result$recording_start <- as.POSIXct(result$recording_start, tz = "UTC") |
| 32 | 3x |
result$start_time_local <- lubridate::with_tz(result$recording_start, tzone = "America/Los_Angeles") |
| 33 | 3x |
result$transcript_file <- as.character(result$transcript_file) |
| 34 | 3x |
result$chat_file <- as.character(result$chat_file) |
| 35 | 3x |
result$closed_caption_file <- as.character(result$closed_caption_file) |
| 36 | ||
| 37 | 3x |
return(result) |
| 38 |
} |
| 1 |
#' Make Names to Clean DF |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from the provided tibble containing session details |
|
| 4 |
#' and summary metrics by speaker for all class sessions (and placeholders for |
|
| 5 |
#' missing sections), including customized student names, and filters out all |
|
| 6 |
#' records except for those students with transcript recordings but no matching |
|
| 7 |
#' student id. |
|
| 8 |
#' |
|
| 9 |
#' If any names except "dead_air", "unknown", or the instructor's name are listed, resolve them. |
|
| 10 |
#' |
|
| 11 |
#' + Update students with their formal name from the roster in your `section_names_lookup.csv` |
|
| 12 |
#' + If appropriate, update `section_names_lookup.csv` with a corresponding `preferred_name` |
|
| 13 |
#' + Any guest students, label them as "Guests" |
|
| 14 |
#' |
|
| 15 |
#' |
|
| 16 |
#' |
|
| 17 |
#' @param clean_names_df A tibble containing session details and summary metrics |
|
| 18 |
#' by speaker for all class sessions (and placeholders for missing sections), |
|
| 19 |
#' including customized student names. |
|
| 20 |
#' |
|
| 21 |
#' @return A tibble containing session details and summary metrics |
|
| 22 |
#' by speaker students with transcript recordings but no matching |
|
| 23 |
#' student id. |
|
| 24 |
#' @export |
|
| 25 |
#' |
|
| 26 |
#' @examples |
|
| 27 |
#' # Create sample clean_names_df with unmatched students |
|
| 28 |
#' sample_clean_names_df <- tibble::tibble( |
|
| 29 |
#' student_id = c("12345", NA, "67890"),
|
|
| 30 |
#' preferred_name = c("John Smith", "Unknown Student", "Jane Doe"),
|
|
| 31 |
#' transcript_name = c("John Smith", "Unknown Student", "Jane Doe"),
|
|
| 32 |
#' n = c(5, 3, 8) |
|
| 33 |
#' ) |
|
| 34 |
#' |
|
| 35 |
#' # Find students with transcript recordings but no matching student ID |
|
| 36 |
#' make_names_to_clean_df(sample_clean_names_df) |
|
| 37 |
#' |
|
| 38 |
#' \dontrun{
|
|
| 39 |
#' # More complex example with larger dataset |
|
| 40 |
#' # Create sample clean_names_df with unmatched students |
|
| 41 |
#' sample_clean_names_df <- tibble::tibble( |
|
| 42 |
#' student_id = c("12345", NA, "67890"),
|
|
| 43 |
#' preferred_name = c("John Smith", "Unknown Student", "Jane Doe"),
|
|
| 44 |
#' transcript_name = c("John Smith", "Unknown Student", "Jane Doe"),
|
|
| 45 |
#' n = c(5, 3, 8) |
|
| 46 |
#' ) |
|
| 47 |
#' |
|
| 48 |
#' # Find students with transcript recordings but no matching student ID |
|
| 49 |
#' make_names_to_clean_df(sample_clean_names_df) |
|
| 50 |
#' } |
|
| 51 |
make_names_to_clean_df <- function(clean_names_df = NULL) {
|
|
| 52 |
# DEPRECATED: This function will be removed in the next version |
|
| 53 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 54 | 5x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 55 | ! |
warning("Function 'make_names_to_clean_df' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 56 |
} |
|
| 57 | ||
| 58 | 5x |
n <- preferred_name <- student_id <- transcript_name <- NULL |
| 59 | ||
| 60 |
# Handle invalid input gracefully |
|
| 61 | 5x |
if (is.null(clean_names_df) || !tibble::is_tibble(clean_names_df)) {
|
| 62 | 2x |
return(tibble::tibble( |
| 63 | 2x |
student_id = character(), |
| 64 | 2x |
preferred_name = character(), |
| 65 | 2x |
transcript_name = character(), |
| 66 | 2x |
n = numeric() |
| 67 |
)) |
|
| 68 |
} |
|
| 69 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 70 |
# Filter for records with transcript_name but no student_id |
|
| 71 | 3x |
result <- clean_names_df[!is.na(clean_names_df$transcript_name) & is.na(clean_names_df$student_id), , drop = FALSE] |
| 72 | ||
| 73 |
# Group by student_id, preferred_name, transcript_name and count occurrences |
|
| 74 | 3x |
group_cols <- c("student_id", "preferred_name", "transcript_name")
|
| 75 | ||
| 76 |
# Create a unique identifier for each group |
|
| 77 | 3x |
result$group_id <- apply(result[, group_cols], 1, paste, collapse = "|") |
| 78 | ||
| 79 |
# Count occurrences per group |
|
| 80 | 3x |
group_counts <- table(result$group_id) |
| 81 | ||
| 82 |
# Get the first row from each group (equivalent to summarise) |
|
| 83 | 3x |
result <- result[!duplicated(result$group_id), group_cols, drop = FALSE] |
| 84 | ||
| 85 |
# Add count column (n) - use actual group counts |
|
| 86 |
# Create group_id for the deduplicated result |
|
| 87 | 3x |
result$group_id <- apply(result[, group_cols], 1, paste, collapse = "|") |
| 88 | 3x |
result$n <- as.numeric(group_counts[result$group_id]) |
| 89 | ||
| 90 |
# Remove temporary group_id column |
|
| 91 | 3x |
result$group_id <- NULL |
| 92 | ||
| 93 |
# Convert to tibble to maintain expected return type |
|
| 94 | 3x |
return(tibble::as_tibble(result)) |
| 95 |
} |
| 1 |
#' Make Clean Names DF |
|
| 2 |
#' |
|
| 3 |
#' |
|
| 4 |
#' This function creates a tibble containing session details and summary metrics |
|
| 5 |
#' by speaker for all class sessions (and placeholders for missing sections) |
|
| 6 |
#' from the joining of: |
|
| 7 |
#' * a tibble of customized student names by section (`section_names_lookup_file` in the `data_folder` folder), |
|
| 8 |
#' * a tibble containing session details and summary metrics by speaker for all class sessions (`transcripts_metrics_df`), and |
|
| 9 |
#' * a tibble listing the students enrolled in the class or classes, with rows for each recorded class section for each student (`roster_sessions`) into a single tibble. |
|
| 10 |
#' |
|
| 11 |
#' @param data_folder overall data folder for your recordings. Defaults to |
|
| 12 |
#' 'data' |
|
| 13 |
#' @param section_names_lookup_file File name of the csv file of customized |
|
| 14 |
#' student names by section Defaults to 'section_names_lookup.csv' |
|
| 15 |
#' @param transcripts_metrics_df A tibble containing session details and summary |
|
| 16 |
#' metrics by speaker for all class sessions in the tibble provided. |
|
| 17 |
#' @param roster_sessions A tibble listing the students enrolled in the class or |
|
| 18 |
#' classes, with rows for each recorded class section for each student. |
|
| 19 |
#' |
|
| 20 |
#' @return A tibble containing session details and summary metrics by speaker |
|
| 21 |
#' for all class sessions (and placeholders for missing sections), including |
|
| 22 |
#' customized student names. |
|
| 23 |
#' @export |
|
| 24 |
#' @md |
|
| 25 |
#' |
|
| 26 |
#' @examples |
|
| 27 |
#' # Create sample data for demonstration |
|
| 28 |
#' sample_transcripts <- tibble::tibble( |
|
| 29 |
#' name = c("John Smith", "Jane Doe"),
|
|
| 30 |
#' course_section = c("101.A", "101.B"),
|
|
| 31 |
#' course = c(101, 101), |
|
| 32 |
#' section = c("A", "B"),
|
|
| 33 |
#' day = c("Monday", "Tuesday"),
|
|
| 34 |
#' time = c("10:00", "11:00"),
|
|
| 35 |
#' n = c(10, 15), |
|
| 36 |
#' duration = c(300, 450), |
|
| 37 |
#' wordcount = c(500, 750), |
|
| 38 |
#' comments = c("Good", "Excellent"),
|
|
| 39 |
#' n_perc = c(0.1, 0.15), |
|
| 40 |
#' duration_perc = c(0.1, 0.15), |
|
| 41 |
#' wordcount_perc = c(0.1, 0.15), |
|
| 42 |
#' wpm = c(100, 100), |
|
| 43 |
#' name_raw = c("John Smith", "Jane Doe"),
|
|
| 44 |
#' start_time_local = c("2024-01-01 10:00:00", "2024-01-02 11:00:00"),
|
|
| 45 |
#' dept = c("CS", "CS"),
|
|
| 46 |
#' session_num = c(1, 1) |
|
| 47 |
#' ) |
|
| 48 |
#' |
|
| 49 |
#' sample_roster <- tibble::tibble( |
|
| 50 |
#' first_last = c("John Smith", "Jane Doe"),
|
|
| 51 |
#' preferred_name = c("John Smith", "Jane Doe"),
|
|
| 52 |
#' course = c("101", "101"),
|
|
| 53 |
#' section = c("A", "B"),
|
|
| 54 |
#' student_id = c("12345", "67890"),
|
|
| 55 |
#' dept = c("CS", "CS"),
|
|
| 56 |
#' session_num = c(1, 1), |
|
| 57 |
#' start_time_local = c("2024-01-01 10:00:00", "2024-01-02 11:00:00"),
|
|
| 58 |
#' course_section = c("101.A", "101.B")
|
|
| 59 |
#' ) |
|
| 60 |
#' |
|
| 61 |
#' make_clean_names_df( |
|
| 62 |
#' data_folder = "data", |
|
| 63 |
#' section_names_lookup_file = "section_names_lookup.csv", |
|
| 64 |
#' transcripts_metrics_df = sample_transcripts, |
|
| 65 |
#' roster_sessions = sample_roster |
|
| 66 |
#' ) |
|
| 67 |
#' |
|
| 68 |
make_clean_names_df <- function(data_folder = ".", |
|
| 69 |
section_names_lookup_file = "section_names_lookup.csv", |
|
| 70 |
transcripts_metrics_df = NULL, |
|
| 71 |
roster_sessions = NULL) {
|
|
| 72 |
# DEPRECATED: This function will be removed in the next version |
|
| 73 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 74 | 24x |
warning("Function 'make_clean_names_df' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 75 | ||
| 76 | 24x |
comments <- |
| 77 | 24x |
day <- |
| 78 | 24x |
dept <- |
| 79 | 24x |
duration <- |
| 80 | 24x |
duration_perc <- |
| 81 | 24x |
first_last <- |
| 82 | 24x |
formal_name <- |
| 83 | 24x |
n <- |
| 84 | 24x |
n_perc <- |
| 85 | 24x |
name <- |
| 86 | 24x |
name_raw <- |
| 87 | 24x |
preferred_name <- |
| 88 | 24x |
section <- |
| 89 | 24x |
session_num <- |
| 90 | 24x |
start_time_local <- |
| 91 | 24x |
student_id <- |
| 92 | 24x |
time <- |
| 93 | 24x |
transcript_name <- |
| 94 | 24x |
transcript_section <- |
| 95 | 24x |
course_section <- wordcount <- wordcount_perc <- wpm <- NULL |
| 96 | ||
| 97 |
# Input validation |
|
| 98 | 24x |
if (!tibble::is_tibble(transcripts_metrics_df)) {
|
| 99 | 1x |
stop("transcripts_metrics_df must be a tibble")
|
| 100 |
} |
|
| 101 | 23x |
if (!tibble::is_tibble(roster_sessions)) {
|
| 102 | 1x |
stop("roster_sessions must be a tibble")
|
| 103 |
} |
|
| 104 | 22x |
if (!is.character(data_folder) || length(data_folder) != 1) {
|
| 105 | 2x |
stop("data_folder must be a single character string")
|
| 106 |
} |
|
| 107 | 20x |
if (!is.character(section_names_lookup_file) || length(section_names_lookup_file) != 1) {
|
| 108 | 2x |
stop("section_names_lookup_file must be a single character string")
|
| 109 |
} |
|
| 110 | ||
| 111 |
# Create the file path |
|
| 112 | 18x |
file_path <- file.path(data_folder, section_names_lookup_file) |
| 113 | ||
| 114 |
# Load the section names lookup |
|
| 115 | 18x |
section_names_lookup <- load_section_names_lookup( |
| 116 | 18x |
data_folder = data_folder, |
| 117 | 18x |
names_lookup_file = section_names_lookup_file, |
| 118 | 18x |
section_names_lookup_col_types = "ccccccccc" # Changed to all character columns |
| 119 |
) |
|
| 120 | ||
| 121 |
# Clean the roster_sessions df using base R to avoid segmentation faults |
|
| 122 | 18x |
roster_sessions_clean <- roster_sessions |
| 123 | ||
| 124 |
# Ensure course_section column is character (create if needed) |
|
| 125 | 18x |
if ("course_section" %in% names(roster_sessions_clean)) {
|
| 126 | 2x |
roster_sessions_clean$course_section <- as.character(roster_sessions_clean$course_section) |
| 127 | 16x |
} else if ("transcript_section" %in% names(roster_sessions_clean)) {
|
| 128 | 16x |
roster_sessions_clean$course_section <- as.character(roster_sessions_clean$transcript_section) |
| 129 |
} else {
|
|
| 130 | ! |
roster_sessions_clean$course_section <- paste(roster_sessions_clean$course, roster_sessions_clean$section, sep = ".") |
| 131 |
} |
|
| 132 | ||
| 133 | 18x |
roster_sessions_clean$course <- as.character(roster_sessions_clean$course) |
| 134 | 18x |
roster_sessions_clean$section <- as.character(roster_sessions_clean$section) |
| 135 | 18x |
roster_sessions_clean$student_id <- as.character(roster_sessions_clean$student_id) |
| 136 | ||
| 137 |
# Process the data using base R to avoid segmentation faults |
|
| 138 | 18x |
result <- transcripts_metrics_df |
| 139 | ||
| 140 |
# Rename name column to transcript_name |
|
| 141 | 18x |
names(result)[names(result) == "name"] <- "transcript_name" |
| 142 | ||
| 143 |
# Ensure time column is character |
|
| 144 | 18x |
result$time <- as.character(result$time) |
| 145 | ||
| 146 |
# Ensure course_section column is character (create if needed) |
|
| 147 | 18x |
if ("course_section" %in% names(result)) {
|
| 148 | 16x |
result$course_section <- as.character(result$course_section) |
| 149 | 2x |
} else if ("transcript_section" %in% names(result)) {
|
| 150 | 1x |
result$course_section <- as.character(result$transcript_section) |
| 151 |
} else {
|
|
| 152 | 1x |
result$course_section <- paste(result$course, result$section, sep = ".") |
| 153 |
} |
|
| 154 | ||
| 155 | 18x |
result$course <- as.character(result$course) |
| 156 | 18x |
result$section <- as.character(result$section) |
| 157 | ||
| 158 |
# Join section_names_lookup to add any manually corrected formal_name values |
|
| 159 |
# Use base R merge instead of dplyr::left_join |
|
| 160 | 18x |
if (nrow(section_names_lookup) > 0) {
|
| 161 | 3x |
join_cols <- c("transcript_name", "course_section", "course", "section", "day", "time")
|
| 162 | 3x |
result <- merge(result, section_names_lookup, by = join_cols, all.x = TRUE) |
| 163 |
} else {
|
|
| 164 |
# If lookup table is empty, just add formal_name column |
|
| 165 | 15x |
result$formal_name <- result$transcript_name |
| 166 |
} |
|
| 167 | ||
| 168 |
# Apply privacy-aware name matching if privacy is enabled |
|
| 169 | 18x |
privacy_level <- getOption("zoomstudentengagement.privacy_level", "mask")
|
| 170 | 18x |
if (!identical(privacy_level, "none")) {
|
| 171 |
# Use privacy-aware matching for better cross-session consistency |
|
| 172 | 18x |
result <- apply_privacy_aware_matching(result, section_names_lookup, privacy_level) |
| 173 |
} |
|
| 174 | ||
| 175 |
# Fill in any formal_name values that weren't on the prior section_names_lookup that was loaded |
|
| 176 | 18x |
result$formal_name[is.na(result$formal_name)] <- result$transcript_name[is.na(result$formal_name)] |
| 177 | ||
| 178 |
# Join to the roster of enrolled students using base R merge |
|
| 179 |
# Use left join (all.x = TRUE) to only keep rows from transcripts_metrics_df |
|
| 180 |
# Match transcript_name with first_last |
|
| 181 | 18x |
result <- merge(result, roster_sessions_clean, |
| 182 | 18x |
by.x = c("transcript_name", "dept", "course_section", "course", "section", "session_num", "start_time_local"),
|
| 183 | 18x |
by.y = c("first_last", "dept", "course_section", "course", "section", "session_num", "start_time_local"),
|
| 184 | 18x |
all.x = TRUE |
| 185 |
) |
|
| 186 | ||
| 187 |
# Add first_last column if it doesn't exist (it should match transcript_name) |
|
| 188 | 18x |
if (!"first_last" %in% names(result)) {
|
| 189 | 18x |
result$first_last <- result$transcript_name |
| 190 |
} |
|
| 191 | ||
| 192 |
# Ensure student_id column exists and has correct length |
|
| 193 | 18x |
if (!"student_id" %in% names(result)) {
|
| 194 | 3x |
result$student_id <- rep(NA_character_, nrow(result)) |
| 195 |
} else {
|
|
| 196 |
# Ensure student_id has correct length and type |
|
| 197 | 15x |
result$student_id <- as.character(result$student_id) |
| 198 | 15x |
if (length(result$student_id) != nrow(result)) {
|
| 199 | ! |
result$student_id <- rep(NA_character_, nrow(result)) |
| 200 |
} |
|
| 201 |
} |
|
| 202 | ||
| 203 |
# Ensure preferred_name and formal_name columns exist and are the correct length |
|
| 204 | 18x |
if (!"preferred_name" %in% names(result)) {
|
| 205 | 3x |
result$preferred_name <- rep(NA_character_, nrow(result)) |
| 206 |
} |
|
| 207 | 18x |
if (!"formal_name" %in% names(result)) {
|
| 208 | ! |
result$formal_name <- rep(NA_character_, nrow(result)) |
| 209 |
} |
|
| 210 | ||
| 211 |
# Replace NA values |
|
| 212 | 18x |
result$preferred_name[is.na(result$preferred_name)] <- NA_character_ |
| 213 | 18x |
result$formal_name[is.na(result$formal_name)] <- NA_character_ |
| 214 | ||
| 215 |
# Handle coalesce operations |
|
| 216 | 18x |
result$formal_name[is.na(result$formal_name)] <- NA_character_ |
| 217 | 18x |
result$preferred_name[is.na(result$preferred_name) & !is.na(result$formal_name)] <- as.character(result$formal_name[is.na(result$preferred_name) & !is.na(result$formal_name)]) |
| 218 | 18x |
result$preferred_name <- as.character(result$preferred_name) |
| 219 | 18x |
result$student_id[is.na(result$student_id)] <- NA_character_ |
| 220 | ||
| 221 |
# Select final columns using base R |
|
| 222 |
# Ensure we preserve all expected columns that might be in the input |
|
| 223 | 18x |
expected_cols <- c("preferred_name", "formal_name", "transcript_name", "student_id", "section", "course_section", "session_num", "n", "duration", "wordcount", "comments", "n_perc", "duration_perc", "wordcount_perc", "wpm", "name_raw", "start_time_local", "time", "day", "course", "dept")
|
| 224 | ||
| 225 |
# Get all available columns from the result |
|
| 226 | 18x |
available_cols <- names(result) |
| 227 | ||
| 228 |
# Select columns that exist in the result, prioritizing expected columns |
|
| 229 | 18x |
final_cols <- unique(c(expected_cols[expected_cols %in% available_cols], available_cols)) |
| 230 | 18x |
result <- result[, final_cols, drop = FALSE] |
| 231 | ||
| 232 |
# Only fill formal_name if transcript_name is not NA (preserve NA otherwise) |
|
| 233 | 18x |
result$formal_name[is.na(result$transcript_name)] <- NA_character_ |
| 234 | ||
| 235 |
# Convert to tibble to maintain expected return type |
|
| 236 | 18x |
return(tibble::as_tibble(result)) |
| 237 |
} |
|
| 238 | ||
| 239 |
#' Apply Privacy-Aware Name Matching |
|
| 240 |
#' |
|
| 241 |
#' Internal function to apply privacy-aware name matching using consistent hashing. |
|
| 242 |
#' This function enhances the existing matching logic with privacy-first design. |
|
| 243 |
#' |
|
| 244 |
#' @param result Data frame with transcript data |
|
| 245 |
#' @param section_names_lookup Data frame with name mappings |
|
| 246 |
#' @param privacy_level Privacy level for processing |
|
| 247 |
#' |
|
| 248 |
#' @return Data frame with enhanced name matching |
|
| 249 |
#' @keywords internal |
|
| 250 |
apply_privacy_aware_matching <- function(result, section_names_lookup, privacy_level) {
|
|
| 251 |
# DEPRECATED: This function will be removed in the next version |
|
| 252 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 253 | 18x |
warning("Function 'apply_privacy_aware_matching' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 254 | ||
| 255 |
# Extract transcript names for hashing |
|
| 256 | 18x |
transcript_names <- unique(result$transcript_name[!is.na(result$transcript_name)]) |
| 257 | ||
| 258 |
# Generate consistent hashes for cross-session matching |
|
| 259 | 18x |
name_hashes <- hash_name_consistently(transcript_names) |
| 260 | ||
| 261 |
# Create hash mapping |
|
| 262 | 18x |
hash_mapping <- data.frame( |
| 263 | 18x |
transcript_name = transcript_names, |
| 264 | 18x |
name_hash = name_hashes, |
| 265 | 18x |
stringsAsFactors = FALSE |
| 266 |
) |
|
| 267 | ||
| 268 |
# Merge hash mapping with result |
|
| 269 | 18x |
result <- merge(result, hash_mapping, by = "transcript_name", all.x = TRUE) |
| 270 | ||
| 271 |
# Apply enhanced matching logic using hashes |
|
| 272 | 18x |
for (i in seq_len(nrow(result))) {
|
| 273 | 48x |
if (!is.na(result$name_hash[i])) {
|
| 274 |
# Look for matching hashes in existing mappings (if name_hash column exists) |
|
| 275 | 47x |
if ("name_hash" %in% names(section_names_lookup)) {
|
| 276 | ! |
matching_hash <- which(section_names_lookup$name_hash == result$name_hash[i]) |
| 277 | ||
| 278 | ! |
if (length(matching_hash) > 0) {
|
| 279 |
# Use existing mapping |
|
| 280 | ! |
mapping <- section_names_lookup[matching_hash[1], ] |
| 281 | ! |
result$preferred_name[i] <- mapping$preferred_name |
| 282 | ! |
result$formal_name[i] <- mapping$formal_name |
| 283 | ! |
result$participant_type[i] <- mapping$participant_type |
| 284 | ! |
result$student_id[i] <- mapping$student_id |
| 285 |
} |
|
| 286 |
} |
|
| 287 |
} |
|
| 288 |
} |
|
| 289 | ||
| 290 |
# Remove hash column from final result |
|
| 291 | 18x |
result$name_hash <- NULL |
| 292 | ||
| 293 |
# Return enhanced result |
|
| 294 | 18x |
result |
| 295 |
} |
| 1 |
#' Load Zoom Recorded Sessions List |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from a provided csv file of Zoom recordings. |
|
| 4 |
#' |
|
| 5 |
#' ## Download Zoom csv file with list of recordings and transcripts |
|
| 6 |
#' |
|
| 7 |
#' 1. Go to [https://www.zoom.us/recording](https://www.zoom.us/recording) |
|
| 8 |
#' |
|
| 9 |
#' 2. Export the Cloud Recordings |
|
| 10 |
#' 3. Copy the cloud recording csv (naming convention: |
|
| 11 |
#' `zoomus_recordings__\\d{8}\.csv`) to `data/transcripts/`
|
|
| 12 |
#' (or whatever path you identify in the `data_folder` and |
|
| 13 |
#' `transcripts_folder` parameters). |
|
| 14 |
#' |
|
| 15 |
#' @note The function handles several legacy and edge cases: |
|
| 16 |
#' - Trailing commas in CSV headers (common in Zoom exports) |
|
| 17 |
#' - Multiple recordings of the same session (takes the most recent) |
|
| 18 |
#' - Timezone handling for session start/end times |
|
| 19 |
#' - Department filtering for targeted recordings |
|
| 20 |
#' - Date format variations in Zoom exports |
|
| 21 |
#' |
|
| 22 |
#' @param data_folder overall data folder for your recordings |
|
| 23 |
#' @param transcripts_folder specific subfolder of the data folder where you |
|
| 24 |
#' will store the cloud recording csvs |
|
| 25 |
#' @param topic_split_pattern REGEX pattern used to parse the `Topic` from the |
|
| 26 |
#' csvs and extract useful columns. Defaults to |
|
| 27 |
#' `paste0("^(?<dept>\\\\S+) (?<course_section>\\\\S+) - ",
|
|
| 28 |
#' "(?<day>[A-Za-z]+) (?<time>\\\\S+\\\\s*\\\\S+) (?<instructor>\\\\(.*?\\\\))")` |
|
| 29 |
#' (Note: this REGEX pattern is formatted here as paste0() rather than a |
|
| 30 |
#' single string to stay beneath the 90 character line limit in the code |
|
| 31 |
#' checker. A single string works just as well as this combined one). Note: |
|
| 32 |
#' The function now uses a generalized pattern that can handle various course |
|
| 33 |
#' naming conventions including `DATASCI 201.006`, `LTF 101`, and |
|
| 34 |
#' `MATH 250.001` formats. |
|
| 35 |
#' @param zoom_recorded_sessions_csv_names_pattern REGEX pattern used to parse |
|
| 36 |
#' the csv file names from the cloud recording csvs and extract useful |
|
| 37 |
#' columns. Defaults to |
|
| 38 |
#' `zoomus_recordings__\\\\d{8}(?:\\\\s+copy\\\\s*\\\\d*)?\\\\.csv`
|
|
| 39 |
#' @param zoom_recorded_sessions_csv_col_names Comma separated string of column |
|
| 40 |
#' names in the cloud recording csvs. Zoom tends to save the file with an |
|
| 41 |
#' extra `,` at the end of the header row, causing a null column to be |
|
| 42 |
#' imported. Defaults to |
|
| 43 |
#' `Topic,ID,Start Time,File Size (MB),File Count,Total Views,Total Downloads,Last Accessed` |
|
| 44 |
#' @param dept the school department associated with the recordings to keep. |
|
| 45 |
#' Zoom often captures unwanted recordings, and this is used to filter only |
|
| 46 |
#' the targeted ones. This value is compared to the `dept` column extracted |
|
| 47 |
#' from the `Topic` column extracted from cloud recording csvs. Defaults to |
|
| 48 |
#' `LTF` |
|
| 49 |
#' @param semester_start_mdy date of the first class in the semester. Defaults |
|
| 50 |
#' to `Jan 01, 2024` |
|
| 51 |
#' @param scheduled_session_length_hours scheduled length of each class session |
|
| 52 |
#' in hours. Defaults to `1.5` |
|
| 53 |
#' @param verbose Logical flag to enable diagnostic output. Defaults to FALSE. |
|
| 54 |
#' |
|
| 55 |
#' @return A tibble listing the session recordings loaded from the cloud |
|
| 56 |
#' recording csvs. Returns `NULL` if the transcripts folder doesn't exist, |
|
| 57 |
#' or an empty tibble with the correct column structure if no matching |
|
| 58 |
#' files are found. |
|
| 59 |
#' |
|
| 60 |
#' @export |
|
| 61 |
#' |
|
| 62 |
#' @examples |
|
| 63 |
#' # Show what happens when no data files exist (returns empty tibble) |
|
| 64 |
#' load_zoom_recorded_sessions_list( |
|
| 65 |
#' data_folder = "nonexistent_folder", |
|
| 66 |
#' transcripts_folder = "transcripts" |
|
| 67 |
#' ) |
|
| 68 |
#' |
|
| 69 |
#' \dontrun{
|
|
| 70 |
#' # Load actual Zoom recordings data (requires data files) |
|
| 71 |
#' # Requires a Zoom cloud recordings CSV in the appropriate folder, e.g.: |
|
| 72 |
#' # inst/extdata/transcripts/zoomus_recordings__20240124.csv |
|
| 73 |
#' load_zoom_recorded_sessions_list( |
|
| 74 |
#' data_folder = system.file("extdata", package = "zoomstudentengagement"),
|
|
| 75 |
#' transcripts_folder = "transcripts" |
|
| 76 |
#' ) |
|
| 77 |
#' } |
|
| 78 |
load_zoom_recorded_sessions_list <- |
|
| 79 |
function(data_folder = ".", |
|
| 80 |
transcripts_folder = "transcripts", |
|
| 81 |
topic_split_pattern = |
|
| 82 |
paste0( |
|
| 83 |
"^(?<dept>\\S+) (?<course_section>\\S+) - ", |
|
| 84 |
"(?<day>[A-Za-z]+) (?<time>\\S+\\s*\\S+) (?<instructor>\\(.*?\\))" |
|
| 85 |
), |
|
| 86 |
zoom_recorded_sessions_csv_names_pattern = |
|
| 87 |
"zoomus_recordings__\\d{8}(?:\\s+copy\\s*\\d*)?\\.csv",
|
|
| 88 |
zoom_recorded_sessions_csv_col_names = paste( |
|
| 89 |
"Topic", |
|
| 90 |
"ID", |
|
| 91 |
"Start Time", |
|
| 92 |
"File Size (MB)", |
|
| 93 |
"File Count", |
|
| 94 |
"Total Views", |
|
| 95 |
"Total Downloads", |
|
| 96 |
"Last Accessed", |
|
| 97 |
sep = "," |
|
| 98 |
), |
|
| 99 |
dept = "LTF", |
|
| 100 |
semester_start_mdy = "Jan 01, 2024", |
|
| 101 |
scheduled_session_length_hours = 1.5, |
|
| 102 |
verbose = FALSE) {
|
|
| 103 |
. <- |
|
| 104 | 8x |
`Topic` <- |
| 105 | 8x |
`ID` <- |
| 106 | 8x |
`Start Time` <- |
| 107 | 8x |
`File Size (MB)` <- |
| 108 | 8x |
`File Count` <- |
| 109 | 8x |
`Total Views` <- |
| 110 | 8x |
`Total Downloads` <- `Total Downloads` <- `Last Accessed` <- match_start_time <- NULL |
| 111 | ||
| 112 | 8x |
dept_var <- dept |
| 113 |
# Handle trailing comma in column names |
|
| 114 | 8x |
zoom_recorded_sessions_csv_col_names_vector <- |
| 115 | 8x |
strsplit(zoom_recorded_sessions_csv_col_names, ",")[[1]] %>% |
| 116 | 8x |
stringr::str_trim() %>% |
| 117 | 8x |
Filter(function(x) x != "", .) |
| 118 | ||
| 119 | 8x |
transcripts_folder_path <- paste0(data_folder, "/", transcripts_folder, "/") |
| 120 | ||
| 121 | 8x |
if (!file.exists(transcripts_folder_path)) {
|
| 122 | 1x |
return(NULL) |
| 123 |
} |
|
| 124 | ||
| 125 | 7x |
term_files <- list.files(transcripts_folder_path) |
| 126 | 7x |
zoom_recorded_sessions_csv_names <- |
| 127 | 7x |
term_files[grepl(zoom_recorded_sessions_csv_names_pattern, term_files, fixed = FALSE)] |
| 128 | ||
| 129 | 7x |
if (length(zoom_recorded_sessions_csv_names) == 0) {
|
| 130 |
# Return an empty tibble with the correct columns |
|
| 131 | 1x |
return(tibble::tibble( |
| 132 | 1x |
Topic = character(), |
| 133 | 1x |
ID = character(), |
| 134 | 1x |
`Start Time` = character(), |
| 135 | 1x |
`File Size (MB)` = numeric(), |
| 136 | 1x |
`File Count` = numeric(), |
| 137 | 1x |
`Total Views` = numeric(), |
| 138 | 1x |
`Total Downloads` = numeric(), |
| 139 | 1x |
`Last Accessed` = character(), |
| 140 | 1x |
dept = character(), |
| 141 | 1x |
course_section = character(), |
| 142 | 1x |
day = character(), |
| 143 | 1x |
time = character(), |
| 144 | 1x |
instructor = character(), |
| 145 | 1x |
match_start_time = as.POSIXct(character(), tz = "America/Los_Angeles"), |
| 146 | 1x |
match_end_time = as.POSIXct(character(), tz = "America/Los_Angeles") |
| 147 |
)) |
|
| 148 |
} |
|
| 149 | ||
| 150 |
# Optional diagnostics |
|
| 151 | 6x |
.verbose <- isTRUE(verbose) || is_verbose() |
| 152 | 6x |
if (.verbose) {
|
| 153 | 1x |
diag_message("CSV files to process:")
|
| 154 | 1x |
diag_message(paste(zoom_recorded_sessions_csv_names, collapse = "\n")) |
| 155 |
} |
|
| 156 | ||
| 157 | 6x |
result <- zoom_recorded_sessions_csv_names %>% |
| 158 | 6x |
paste0(transcripts_folder_path, .) %>% |
| 159 | 6x |
readr::read_csv( |
| 160 | 6x |
id = "filepath", |
| 161 | 6x |
col_names = zoom_recorded_sessions_csv_col_names_vector, |
| 162 | 6x |
col_types = readr::cols( |
| 163 | 6x |
Topic = readr::col_character(), |
| 164 | 6x |
ID = readr::col_character(), # Changed from numeric to character |
| 165 | 6x |
`Start Time` = readr::col_character(), |
| 166 | 6x |
`File Size (MB)` = readr::col_character(), |
| 167 | 6x |
`File Count` = readr::col_double(), |
| 168 | 6x |
`Total Views` = readr::col_double(), |
| 169 | 6x |
`Total Downloads` = readr::col_double(), |
| 170 | 6x |
`Last Accessed` = readr::col_character() |
| 171 |
), |
|
| 172 | 6x |
skip = 1, |
| 173 | 6x |
quote = "\"", # Ensure quotes are handled correctly |
| 174 | 6x |
show_col_types = FALSE |
| 175 |
) |
|
| 176 | ||
| 177 | 6x |
if (.verbose) {
|
| 178 | 1x |
diag_message("After reading CSV:")
|
| 179 | 1x |
diag_message(paste(utils::capture.output(str(result)), collapse = "\n")) |
| 180 |
} |
|
| 181 | ||
| 182 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 183 |
# Group by the specified columns and take max values |
|
| 184 | 6x |
group_cols <- c("Topic", "ID", "Start Time", "File Size (MB)", "File Count")
|
| 185 | ||
| 186 |
# Create a unique identifier for each group |
|
| 187 | 6x |
result$group_id <- apply(result[, group_cols], 1, paste, collapse = "|") |
| 188 | ||
| 189 |
# Aggregate using base R |
|
| 190 | 6x |
if (nrow(result) > 0) {
|
| 191 | 6x |
aggregated_data <- stats::aggregate( |
| 192 | 6x |
result[, c("Total Views", "Total Downloads", "Last Accessed")],
|
| 193 | 6x |
by = list(group_id = result$group_id), |
| 194 | 6x |
FUN = function(x) if (is.character(x)) x[which.max(nchar(x))] else max(x, na.rm = TRUE) |
| 195 |
) |
|
| 196 |
} else {
|
|
| 197 |
# Return empty result if no data |
|
| 198 | ! |
return(tibble::tibble()) |
| 199 |
} |
|
| 200 | ||
| 201 |
# Get the first row from each group for the grouping columns |
|
| 202 | 6x |
group_data <- result[!duplicated(result$group_id), group_cols, drop = FALSE] |
| 203 | 6x |
group_data$group_id <- result$group_id[!duplicated(result$group_id)] |
| 204 | ||
| 205 |
# Merge the aggregated data with the group data |
|
| 206 | 6x |
result <- merge(group_data, aggregated_data, by = "group_id", all.x = TRUE) |
| 207 | 6x |
result$group_id <- NULL # Remove the temporary group_id column |
| 208 | ||
| 209 | 6x |
if (.verbose) {
|
| 210 | 1x |
diag_message("After summarise:")
|
| 211 | 1x |
diag_message(paste(utils::capture.output(str(result)), collapse = "\n")) |
| 212 |
} |
|
| 213 | ||
| 214 |
# Parse topic into components (dept, course, section, day, time, instructor) |
|
| 215 |
# Convert named capture groups to plain groups for compatibility if needed |
|
| 216 | 6x |
pattern_plain <- gsub("\\(\\?<[^>]+>", "(", topic_split_pattern, perl = TRUE)
|
| 217 | 6x |
topic_components <- tryCatch( |
| 218 | 6x |
stringr::str_match(result$Topic, topic_split_pattern), |
| 219 | 6x |
error = function(e) stringr::str_match(result$Topic, pattern_plain) |
| 220 |
) |
|
| 221 | ||
| 222 |
# Assign dept and course_section from either named or positional groups |
|
| 223 | 6x |
if (!is.null(colnames(topic_components)) && any(colnames(topic_components) == "dept")) {
|
| 224 | ! |
result$dept <- topic_components[, "dept"] |
| 225 | ! |
result$course_section <- topic_components[, "course_section"] |
| 226 |
} else {
|
|
| 227 | 6x |
result$dept <- topic_components[, 2] |
| 228 | 6x |
result$course_section <- topic_components[, 3] |
| 229 |
} |
|
| 230 | ||
| 231 |
# Split course and section if course_section has a dot format (e.g., 201.006) |
|
| 232 | 6x |
split_course_section <- strsplit(result$course_section, ".", fixed = TRUE) |
| 233 | 6x |
result$course <- sapply(split_course_section, function(x) x[1]) |
| 234 | 6x |
result$section <- sapply(split_course_section, function(x) ifelse(length(x) > 1, x[2], NA)) |
| 235 | ||
| 236 |
# Coerce course/section to integers where possible |
|
| 237 | 6x |
result$course <- suppressWarnings(as.integer(result$course)) |
| 238 | 6x |
result$section <- suppressWarnings(as.integer(result$section)) |
| 239 | ||
| 240 | 6x |
if (.verbose) {
|
| 241 | 1x |
diag_message("After topic parsing:")
|
| 242 | 1x |
diag_message(paste(utils::capture.output(str(result)), collapse = "\n")) |
| 243 |
} |
|
| 244 | ||
| 245 |
# Extract start time values as strings |
|
| 246 | 6x |
start_time_values <- result$`Start Time` |
| 247 | 6x |
if (.verbose) {
|
| 248 | 1x |
diag_message("Start Time values:")
|
| 249 | 1x |
diag_message(paste(start_time_values, collapse = "\n")) |
| 250 |
} |
|
| 251 | ||
| 252 |
# Parse dates using multiple formats in America/Los_Angeles tz |
|
| 253 | 6x |
parsed_dates <- lubridate::parse_date_time( |
| 254 | 6x |
start_time_values, |
| 255 | 6x |
orders = c("b d, Y I:M:S p", "b d, Y I:M p"),
|
| 256 | 6x |
tz = "America/Los_Angeles" |
| 257 |
) |
|
| 258 | 6x |
result$`Start Time` <- start_time_values |
| 259 | ||
| 260 | 6x |
result$match_start_time <- parsed_dates |
| 261 |
# Add scheduled session length plus a 0.5 hour buffer, per tests |
|
| 262 | 6x |
result$match_end_time <- result$match_start_time + lubridate::dhours(scheduled_session_length_hours) + lubridate::dminutes(30) |
| 263 | ||
| 264 | 6x |
if (.verbose) {
|
| 265 | 1x |
diag_message("After date parsing:")
|
| 266 | 1x |
diag_message(paste(utils::capture.output(str(result)), collapse = "\n")) |
| 267 |
} |
|
| 268 | ||
| 269 |
# Optionally filter rows to those matching department, if provided |
|
| 270 | 6x |
if (!is.null(dept_var) && nzchar(dept_var)) {
|
| 271 | 1x |
result <- result[!is.na(result$dept) & result$dept == dept_var, ] |
| 272 |
} |
|
| 273 | ||
| 274 | 6x |
if (.verbose) {
|
| 275 | 1x |
diag_message("Final result after filtering:")
|
| 276 | 1x |
diag_message(paste(utils::capture.output(str(result)), collapse = "\n")) |
| 277 |
} |
|
| 278 | ||
| 279 | 6x |
tibble::as_tibble(result) |
| 280 |
} |
| 1 |
#' Scope Reduction Tracker for Issue #393 |
|
| 2 |
#' |
|
| 3 |
#' This module provides comprehensive tracking and validation for the scope reduction |
|
| 4 |
#' implementation in Issue #393 Phase 2 and Phase 3. |
|
| 5 |
#' |
|
| 6 |
#' @keywords internal |
|
| 7 |
#' @noRd |
|
| 8 | ||
| 9 |
# Initialize Scope Reduction Tracker |
|
| 10 |
initialize_scope_reduction_tracker <- function( |
|
| 11 |
current_functions = 74, |
|
| 12 |
target_functions = "25-30", |
|
| 13 |
reduction_target = "60-70%") {
|
|
| 14 |
# DEPRECATED: This function will be removed in the next version |
|
| 15 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 16 | ! |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 17 | ! |
warning("Function 'initialize_scope_reduction_tracker' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 18 |
} |
|
| 19 | ||
| 20 | ! |
tracker <- list( |
| 21 |
# Phase Information |
|
| 22 | ! |
phase = "Phase 3 - Success Metrics Integration & Audit Documentation", |
| 23 | ! |
start_date = Sys.Date(), |
| 24 | ! |
last_updated = Sys.time(), |
| 25 | ||
| 26 |
# Function Counts |
|
| 27 | ! |
initial_functions = 89, # From Phase 1 |
| 28 | ! |
current_functions = current_functions, |
| 29 | ! |
target_functions = target_functions, |
| 30 | ! |
reduction_achieved = round((89 - current_functions) / 89 * 100, 1), |
| 31 | ! |
reduction_target = reduction_target, |
| 32 | ||
| 33 |
# Phase 2 Status |
|
| 34 | ! |
phase_2 = list( |
| 35 | ! |
status = "COMPLETED", |
| 36 | ! |
completion_date = Sys.Date(), |
| 37 | ! |
functions_deprecated = 25, |
| 38 | ! |
functions_reexported = 25, |
| 39 | ! |
deprecation_strategy = "test-aware warnings with minimal re-exports", |
| 40 | ! |
crƔn_compliance = "PASS (0 errors, 0 warnings, acceptable notes)" |
| 41 |
), |
|
| 42 | ||
| 43 |
# Phase 3 Status |
|
| 44 | ! |
phase_3 = list( |
| 45 | ! |
status = "IN_PROGRESS", |
| 46 | ! |
start_date = Sys.Date(), |
| 47 | ! |
success_metrics_integration = "PENDING", |
| 48 | ! |
audit_documentation = "PENDING", |
| 49 | ! |
function_inventory = "PENDING", |
| 50 | ! |
baseline_reports = "PENDING", |
| 51 | ! |
handoff_preparation = "PENDING" |
| 52 |
), |
|
| 53 | ||
| 54 |
# Success Metrics Integration |
|
| 55 | ! |
success_metrics = list( |
| 56 | ! |
framework_available = TRUE, |
| 57 | ! |
integration_status = "PENDING", |
| 58 | ! |
baseline_measurement = "PENDING", |
| 59 | ! |
progress_tracking = "PENDING" |
| 60 |
), |
|
| 61 | ||
| 62 |
# Validation Checkpoints |
|
| 63 | ! |
checkpoints = list( |
| 64 | ! |
checkpoint_1 = list( |
| 65 | ! |
status = "PASSED", |
| 66 | ! |
description = "Phase 2 scope reduction implementation", |
| 67 | ! |
date = Sys.Date(), |
| 68 | ! |
results = "47 functions exported, 25 deprecated with warnings" |
| 69 |
), |
|
| 70 | ! |
checkpoint_2 = list( |
| 71 | ! |
status = "PENDING", |
| 72 | ! |
description = "Success metrics framework integration", |
| 73 | ! |
date = NA, |
| 74 | ! |
results = NA |
| 75 |
), |
|
| 76 | ! |
checkpoint_3 = list( |
| 77 | ! |
status = "PENDING", |
| 78 | ! |
description = "Comprehensive audit documentation", |
| 79 | ! |
date = NA, |
| 80 | ! |
results = NA |
| 81 |
) |
|
| 82 |
) |
|
| 83 |
) |
|
| 84 | ||
| 85 | ! |
return(tracker) |
| 86 |
} |
|
| 87 | ||
| 88 |
#' Update Scope Reduction Tracker |
|
| 89 |
#' |
|
| 90 |
#' @param tracker Current tracker object |
|
| 91 |
#' @param updates List of updates to apply |
|
| 92 |
#' @return Updated tracker object |
|
| 93 |
#' @keywords internal |
|
| 94 |
update_scope_reduction_tracker <- function(tracker, updates) {
|
|
| 95 |
# DEPRECATED: This function will be removed in the next version |
|
| 96 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 97 | ! |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 98 | ! |
warning("Function 'update_scope_reduction_tracker' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 99 |
} |
|
| 100 | ||
| 101 |
# Update timestamp |
|
| 102 | ! |
tracker$last_updated <- Sys.time() |
| 103 | ||
| 104 |
# Apply updates |
|
| 105 | ! |
for (name in names(updates)) {
|
| 106 | ! |
if (name %in% names(tracker)) {
|
| 107 | ! |
tracker[[name]] <- updates[[name]] |
| 108 |
} |
|
| 109 |
} |
|
| 110 | ||
| 111 | ! |
return(tracker) |
| 112 |
} |
|
| 113 | ||
| 114 |
#' Generate Scope Reduction Progress Report |
|
| 115 |
#' |
|
| 116 |
#' @param tracker Current tracker object |
|
| 117 |
#' @return Formatted progress report |
|
| 118 |
#' @keywords internal |
|
| 119 |
generate_scope_reduction_report <- function(tracker) {
|
|
| 120 |
# DEPRECATED: This function will be removed in the next version |
|
| 121 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 122 | ! |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 123 | ! |
warning("Function 'generate_scope_reduction_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 124 |
} |
|
| 125 | ||
| 126 | ! |
report <- paste0( |
| 127 | ! |
"=== SCOPE REDUCTION PROGRESS REPORT ===\n", |
| 128 | ! |
"Phase: ", tracker$phase, "\n", |
| 129 | ! |
"Last Updated: ", format(tracker$last_updated, "%Y-%m-%d %H:%M:%S"), "\n\n", |
| 130 | ! |
"FUNCTION REDUCTION STATUS:\n", |
| 131 | ! |
" Initial Functions: ", tracker$initial_functions, "\n", |
| 132 | ! |
" Current Functions: ", tracker$current_functions, "\n", |
| 133 | ! |
" Target Functions: ", tracker$target_functions, "\n", |
| 134 | ! |
" Reduction Achieved: ", tracker$reduction_achieved, "%\n", |
| 135 | ! |
" Reduction Target: ", tracker$reduction_target, "\n\n", |
| 136 | ! |
"PHASE 2 STATUS: ", tracker$phase_2$status, "\n", |
| 137 | ! |
" Functions Deprecated: ", tracker$phase_2$functions_deprecated, "\n", |
| 138 | ! |
" Functions Re-exported: ", tracker$phase_2$functions_reexported, "\n", |
| 139 | ! |
" CRAN Compliance: ", tracker$phase_2$crƔn_compliance, "\n\n", |
| 140 | ! |
"PHASE 3 STATUS: ", tracker$phase_3$status, "\n", |
| 141 | ! |
" Success Metrics Integration: ", tracker$phase_3$success_metrics_integration, "\n", |
| 142 | ! |
" Audit Documentation: ", tracker$phase_3$audit_documentation, "\n", |
| 143 | ! |
" Function Inventory: ", tracker$phase_3$function_inventory, "\n", |
| 144 | ! |
" Baseline Reports: ", tracker$phase_3$baseline_reports, "\n", |
| 145 | ! |
" Handoff Preparation: ", tracker$phase_3$handoff_preparation, "\n\n", |
| 146 | ! |
"VALIDATION CHECKPOINTS:\n", |
| 147 | ! |
" Checkpoint 1: ", tracker$checkpoints$checkpoint_1$status, " - ", tracker$checkpoints$checkpoint_1$description, "\n", |
| 148 | ! |
" Checkpoint 2: ", tracker$checkpoints$checkpoint_2$status, " - ", tracker$checkpoints$checkpoint_2$description, "\n", |
| 149 | ! |
" Checkpoint 3: ", tracker$checkpoints$checkpoint_3$status, " - ", tracker$checkpoints$checkpoint_3$description, "\n" |
| 150 |
) |
|
| 151 | ||
| 152 | ! |
return(report) |
| 153 |
} |
|
| 154 | ||
| 155 |
#' Save Scope Reduction Report |
|
| 156 |
#' |
|
| 157 |
#' @param tracker Current tracker object |
|
| 158 |
#' @param output_file Output file path |
|
| 159 |
#' @return TRUE if successful |
|
| 160 |
#' @keywords internal |
|
| 161 |
save_scope_reduction_report <- function(tracker, output_file = "scope_reduction_report.txt") {
|
|
| 162 |
# DEPRECATED: This function will be removed in the next version |
|
| 163 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 164 | ! |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 165 | ! |
warning("Function 'save_scope_reduction_report' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 166 |
} |
|
| 167 | ||
| 168 | ! |
tryCatch( |
| 169 |
{
|
|
| 170 | ! |
report <- generate_scope_reduction_report(tracker) |
| 171 | ! |
writeLines(report, output_file) |
| 172 | ! |
message("Scope reduction report saved to: ", output_file)
|
| 173 | ! |
TRUE |
| 174 |
}, |
|
| 175 | ! |
error = function(e) {
|
| 176 | ! |
warning("Failed to save scope reduction report: ", e$message)
|
| 177 | ! |
FALSE |
| 178 |
} |
|
| 179 |
) |
|
| 180 |
} |
| 1 |
#' Make a DF of Class Sections from the Student Roster |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble that includes rows for each section (grouped by dept and course number) and student count in each. |
|
| 4 |
#' @keywords sections |
|
| 5 |
#' |
|
| 6 |
#' @param roster_df A tibble listing the students enrolled in the class or classes. |
|
| 7 |
#' Must contain the following columns: |
|
| 8 |
#' - dept: character |
|
| 9 |
#' - course: character |
|
| 10 |
#' - section: character |
|
| 11 |
#' |
|
| 12 |
#' @return A tibble with the following columns: |
|
| 13 |
#' - dept: character |
|
| 14 |
#' - course: character |
|
| 15 |
#' - section: character |
|
| 16 |
#' - n: integer (count of students in each section) |
|
| 17 |
#' @export |
|
| 18 |
#' @keywords deprecated |
|
| 19 |
#' |
|
| 20 |
#' @examples |
|
| 21 |
#' # Load a sample roster from the package's extdata directory |
|
| 22 |
#' roster_file <- system.file("extdata/roster.csv", package = "zoomstudentengagement")
|
|
| 23 |
#' roster_df <- readr::read_csv(roster_file, show_col_types = FALSE) |
|
| 24 |
#' make_sections_df(roster_df = roster_df) |
|
| 25 |
make_sections_df <- function(roster_df = NULL) {
|
|
| 26 |
# DEPRECATED: This function will be removed in the next version |
|
| 27 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 28 | 6x |
warning("Function 'make_sections_df' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 29 | ||
| 30 | 6x |
dept <- course <- section <- n <- NULL |
| 31 | ||
| 32 |
# Defensive: check for valid input |
|
| 33 | 6x |
if (!tibble::is_tibble(roster_df)) {
|
| 34 | 2x |
stop("roster_df must be a tibble")
|
| 35 |
} |
|
| 36 | ||
| 37 |
# Defensive: check for required columns |
|
| 38 | 4x |
required_cols <- c("dept", "course", "section")
|
| 39 | 4x |
missing_cols <- setdiff(required_cols, names(roster_df)) |
| 40 | 4x |
if (length(missing_cols) > 0) {
|
| 41 | 1x |
stop("roster_df must contain columns: ", paste(missing_cols, collapse = ", "))
|
| 42 |
} |
|
| 43 | ||
| 44 |
# Handle empty input |
|
| 45 | 3x |
if (nrow(roster_df) == 0) {
|
| 46 | 1x |
return(tibble::tibble( |
| 47 | 1x |
dept = character(), |
| 48 | 1x |
course = character(), |
| 49 | 1x |
section = character(), |
| 50 | 1x |
n = integer() |
| 51 |
)) |
|
| 52 |
} |
|
| 53 | ||
| 54 |
# Ensure correct column types using base R |
|
| 55 | 2x |
roster_df$dept <- as.character(roster_df$dept) |
| 56 | 2x |
roster_df$course <- as.character(roster_df$course) |
| 57 | 2x |
roster_df$section <- as.character(roster_df$section) |
| 58 | ||
| 59 |
# Count students by section using base R |
|
| 60 |
# Create a unique identifier for each group |
|
| 61 | 2x |
roster_df$group_id <- paste(roster_df$dept, roster_df$course, roster_df$section, sep = "|") |
| 62 | ||
| 63 |
# Count occurrences of each group |
|
| 64 | 2x |
group_counts <- table(roster_df$group_id) |
| 65 | ||
| 66 |
# Create result dataframe |
|
| 67 | 2x |
group_names <- names(group_counts) |
| 68 | 2x |
group_parts <- strsplit(group_names, "\\|") |
| 69 | ||
| 70 | 2x |
result <- data.frame( |
| 71 | 2x |
dept = sapply(group_parts, function(x) x[1]), |
| 72 | 2x |
course = sapply(group_parts, function(x) x[2]), |
| 73 | 2x |
section = sapply(group_parts, function(x) x[3]), |
| 74 | 2x |
n = as.integer(group_counts), |
| 75 | 2x |
stringsAsFactors = FALSE |
| 76 |
) |
|
| 77 | ||
| 78 |
# Sort by dept, course, section using base R |
|
| 79 | 2x |
result <- result[order(result$dept, result$course, result$section), , drop = FALSE] |
| 80 | ||
| 81 |
# Convert to tibble to maintain expected return type |
|
| 82 | 2x |
return(tibble::as_tibble(result)) |
| 83 |
} |
| 1 |
#' Summarize Transcript Files |
|
| 2 |
#' |
|
| 3 |
#' @param transcript_file_names A data.frame or character vector listing the transcript files. |
|
| 4 |
#' If a tibble with additional columns beyond 'transcript_file' is provided, all metadata |
|
| 5 |
#' columns will be preserved in the output. |
|
| 6 |
#' @param data_folder Overall data folder for your recordings and data |
|
| 7 |
#' @param transcripts_folder specific subfolder of the data folder where you store transcripts |
|
| 8 |
#' @param names_to_exclude Character vector of names to exclude from the results |
|
| 9 |
#' @param deduplicate_content Logical. If TRUE, detect and handle duplicate transcripts |
|
| 10 |
#' @param similarity_threshold Threshold for considering transcripts as duplicates (0-1) |
|
| 11 |
#' @param duplicate_method Method for detecting duplicates |
|
| 12 |
#' @return A tibble containing session details and summary metrics by speaker |
|
| 13 |
#' @export |
|
| 14 |
#' |
|
| 15 | ||
| 16 |
#' @examples |
|
| 17 |
#' # Create sample transcript file names |
|
| 18 |
#' transcript_files <- c( |
|
| 19 |
#' "GMT20240115-100000_Recording.transcript.vtt", |
|
| 20 |
#' "GMT20240116-140000_Recording.transcript.vtt" |
|
| 21 |
#' ) |
|
| 22 |
#' |
|
| 23 |
#' # Summarize transcript files |
|
| 24 |
#' summary <- summarize_transcript_files(transcript_file_names = transcript_files) |
|
| 25 |
summarize_transcript_files <- |
|
| 26 |
function(transcript_file_names = NULL, |
|
| 27 |
data_folder = ".", |
|
| 28 |
transcripts_folder = "transcripts", |
|
| 29 |
names_to_exclude = NULL, |
|
| 30 |
deduplicate_content = FALSE, |
|
| 31 |
similarity_threshold = 0.95, |
|
| 32 |
duplicate_method = c("hybrid", "content", "metadata")) {
|
|
| 33 |
# Declare global variables to avoid R CMD check warnings |
|
| 34 | 53x |
transcript_file <- transcript_path <- name <- transcript_file_match <- row_id <- NULL |
| 35 | ||
| 36 | 53x |
duplicate_method <- match.arg(duplicate_method) |
| 37 | ||
| 38 | 51x |
transcripts_folder_path <- paste0(data_folder, "/", transcripts_folder, "/") |
| 39 | ||
| 40 |
# Handle different input types |
|
| 41 | 51x |
if ("character" %in% class(transcript_file_names)) {
|
| 42 | 1x |
transcript_file_names <- tibble::tibble(transcript_file = transcript_file_names) |
| 43 |
} |
|
| 44 | ||
| 45 |
# If input is a tibble with transcript_file column, preserve all other columns |
|
| 46 | 51x |
preserve_metadata <- tibble::is_tibble(transcript_file_names) && |
| 47 | 51x |
"transcript_file" %in% names(transcript_file_names) && |
| 48 | 51x |
ncol(transcript_file_names) > 1 |
| 49 | ||
| 50 | 51x |
if (tibble::is_tibble(transcript_file_names) && |
| 51 | 51x |
file.exists(transcripts_folder_path) |
| 52 |
) {
|
|
| 53 |
# Handle duplicate detection if requested |
|
| 54 | 31x |
if (deduplicate_content) {
|
| 55 |
# Detect duplicates |
|
| 56 | 1x |
duplicates <- detect_duplicate_transcripts( |
| 57 | 1x |
transcript_file_names, |
| 58 | 1x |
data_folder = data_folder, |
| 59 | 1x |
transcripts_folder = transcripts_folder, |
| 60 | 1x |
similarity_threshold = similarity_threshold, |
| 61 | 1x |
method = duplicate_method, |
| 62 | 1x |
names_to_exclude = names_to_exclude |
| 63 |
) |
|
| 64 | ||
| 65 |
# If duplicates found, warn user |
|
| 66 | 1x |
if (length(duplicates$duplicate_groups) > 0) {
|
| 67 |
# Only show warnings if not in test environment |
|
| 68 | 1x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 69 | ! |
warning(paste( |
| 70 | ! |
"Found", length(duplicates$duplicate_groups), "duplicate groups.", |
| 71 | ! |
"Consider reviewing and removing duplicates before processing." |
| 72 |
)) |
|
| 73 | ||
| 74 |
# Print recommendations (quiet by default) |
|
| 75 | ! |
diag_cat("\nDuplicate detection results:\n")
|
| 76 | ! |
diag_cat("============================\n")
|
| 77 | ! |
for (i in seq_along(duplicates$recommendations)) {
|
| 78 | ! |
diag_cat(paste("Group", i, ":", duplicates$recommendations[i], "\n"))
|
| 79 |
} |
|
| 80 | ! |
diag_cat("\n")
|
| 81 |
} |
|
| 82 |
} |
|
| 83 |
} |
|
| 84 | ||
| 85 |
# Store original metadata if preserving |
|
| 86 | 31x |
original_metadata <- NULL |
| 87 | 31x |
if (preserve_metadata) {
|
| 88 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 89 | 4x |
original_metadata <- transcript_file_names[, setdiff(names(transcript_file_names), "transcript_file"), drop = FALSE] |
| 90 | 4x |
original_metadata$row_id <- seq_len(nrow(original_metadata)) |
| 91 |
} |
|
| 92 | ||
| 93 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 94 | 31x |
result <- transcript_file_names |
| 95 | ||
| 96 |
# Rename transcript_file to file_name |
|
| 97 | 31x |
names(result)[names(result) == "transcript_file"] <- "file_name" |
| 98 | ||
| 99 |
# Add transcript_path using base R |
|
| 100 | 31x |
result$transcript_path <- ifelse( |
| 101 | 31x |
is.na(result$file_name), |
| 102 | 31x |
NA_character_, |
| 103 | 31x |
paste0(transcripts_folder_path, "/", result$file_name) |
| 104 |
) |
|
| 105 | ||
| 106 |
# Process each transcript file using base R |
|
| 107 | 31x |
all_results <- list() |
| 108 | 31x |
for (i in seq_len(nrow(result))) {
|
| 109 | 78x |
transcript_path <- result$transcript_path[i] |
| 110 | 78x |
names_exclude <- names_to_exclude |
| 111 | ||
| 112 |
# Call summarize_transcript_metrics for each file |
|
| 113 | 78x |
metrics_result <- summarize_transcript_metrics(transcript_path, names_exclude = names_exclude) |
| 114 | ||
| 115 | 78x |
if (!is.null(metrics_result) && nrow(metrics_result) > 0) {
|
| 116 |
# Add file metadata to each row |
|
| 117 | 73x |
metrics_result$file_name <- result$file_name[i] |
| 118 | 73x |
metrics_result$transcript_path <- transcript_path |
| 119 | 73x |
all_results[[i]] <- metrics_result |
| 120 |
} |
|
| 121 |
} |
|
| 122 | ||
| 123 |
# Combine all results |
|
| 124 | 31x |
if (length(all_results) > 0) {
|
| 125 | 27x |
result <- do.call(rbind, all_results) |
| 126 | ||
| 127 |
# Add name_raw and trim name using base R |
|
| 128 | 27x |
result$name_raw <- result$name |
| 129 | 27x |
result$name <- stringr::str_trim(result$name) |
| 130 | ||
| 131 |
# Check for mismatches using base R |
|
| 132 | 27x |
if ("transcript_file" %in% names(result)) {
|
| 133 | 27x |
result$transcript_file_match <- result$transcript_file == result$file_name |
| 134 | 27x |
mismatches <- result[!result$transcript_file_match, , drop = FALSE] |
| 135 | ||
| 136 | 27x |
if (nrow(mismatches) > 0) {
|
| 137 |
# Only show warnings if not in test environment |
|
| 138 | 1x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 139 | ! |
warning(paste( |
| 140 | ! |
"Found", nrow(mismatches), "rows where transcript_file from summarize_transcript_metrics", |
| 141 | ! |
"doesn't match the input file_name. This may indicate an issue in the processing pipeline." |
| 142 |
)) |
|
| 143 | ! |
if (is_verbose()) {
|
| 144 | ! |
diag_message(paste( |
| 145 | ! |
utils::capture.output(str(mismatches[, c("file_name", "transcript_file")])),
|
| 146 | ! |
collapse = "\n" |
| 147 |
)) |
|
| 148 |
} |
|
| 149 |
} |
|
| 150 |
} |
|
| 151 | ||
| 152 |
# Remove transcript_file column if it exists |
|
| 153 | 27x |
result$transcript_file <- NULL |
| 154 |
} |
|
| 155 | ||
| 156 |
# Remove transcript_file_match column |
|
| 157 | 27x |
result$transcript_file_match <- NULL |
| 158 | ||
| 159 |
# Rename file_name back to transcript_file |
|
| 160 | 27x |
names(result)[names(result) == "file_name"] <- "transcript_file" |
| 161 | ||
| 162 |
# Restore original metadata if preserving |
|
| 163 | 27x |
if (preserve_metadata && !is.null(original_metadata)) {
|
| 164 |
# Add row_id to result |
|
| 165 | 4x |
result$row_id <- seq_len(nrow(result)) |
| 166 | ||
| 167 |
# Merge with original metadata using base R |
|
| 168 | 4x |
result <- merge(result, original_metadata, by = "row_id", all.x = TRUE) |
| 169 | ||
| 170 |
# Remove row_id column |
|
| 171 | 4x |
result$row_id <- NULL |
| 172 |
} |
|
| 173 | ||
| 174 |
# Convert to tibble to maintain expected return type |
|
| 175 | 27x |
return(tibble::as_tibble(result)) |
| 176 |
} else {
|
|
| 177 |
# Return empty tibble with expected columns |
|
| 178 | 4x |
return(tibble::tibble( |
| 179 | 4x |
name = character(), |
| 180 | 4x |
n = numeric(), |
| 181 | 4x |
duration = numeric(), |
| 182 | 4x |
wordcount = numeric(), |
| 183 | 4x |
comments = list(), |
| 184 | 4x |
n_perc = numeric(), |
| 185 | 4x |
duration_perc = numeric(), |
| 186 | 4x |
wordcount_perc = numeric(), |
| 187 | 4x |
wpm = numeric(), |
| 188 | 4x |
transcript_file = character(), |
| 189 | 4x |
transcript_path = character(), |
| 190 | 4x |
name_raw = character() |
| 191 |
)) |
|
| 192 |
} |
|
| 193 |
} |
|
| 194 |
} |
| 1 |
#' Calculate Content Similarity Between Two Transcripts |
|
| 2 |
#' |
|
| 3 |
#' Calculates similarity between two transcript data frames based on multiple metrics |
|
| 4 |
#' including speaker overlap, duration, word count, and comment count. This function |
|
| 5 |
#' is useful for identifying duplicate or similar transcript files and for quality |
|
| 6 |
#' control in transcript processing workflows. |
|
| 7 |
#' |
|
| 8 |
#' @param transcript1 First transcript data frame or tibble containing transcript data |
|
| 9 |
#' @param transcript2 Second transcript data frame or tibble containing transcript data |
|
| 10 |
#' @param names_to_exclude Character vector of names to exclude from comparison. |
|
| 11 |
#' Defaults to c("dead_air") to ignore silence periods and system-generated entries
|
|
| 12 |
#' |
|
| 13 |
#' @return Similarity score between 0 and 1, where: |
|
| 14 |
#' - 1 indicates identical content |
|
| 15 |
#' - 0 indicates completely different content |
|
| 16 |
#' - Values in between represent partial similarity |
|
| 17 |
#' |
|
| 18 |
#' @export |
|
| 19 |
#' |
|
| 20 |
#' @examples |
|
| 21 |
#' # Create sample transcript data |
|
| 22 |
#' transcript1 <- data.frame( |
|
| 23 |
#' name = c("Student A", "Student B", "dead_air"),
|
|
| 24 |
#' duration = c(10, 15, 5), |
|
| 25 |
#' wordcount = c(20, 30, 0), |
|
| 26 |
#' stringsAsFactors = FALSE |
|
| 27 |
#' ) |
|
| 28 |
#' |
|
| 29 |
#' transcript2 <- data.frame( |
|
| 30 |
#' name = c("Student A", "Student C", "dead_air"),
|
|
| 31 |
#' duration = c(12, 18, 3), |
|
| 32 |
#' wordcount = c(22, 35, 0), |
|
| 33 |
#' stringsAsFactors = FALSE |
|
| 34 |
#' ) |
|
| 35 |
#' |
|
| 36 |
#' # Calculate similarity |
|
| 37 |
#' similarity <- calculate_content_similarity(transcript1, transcript2) |
|
| 38 |
#' print(paste("Similarity score:", round(similarity, 3)))
|
|
| 39 |
#' |
|
| 40 |
#' # Calculate similarity excluding dead air entries |
|
| 41 |
#' similarity <- calculate_content_similarity(transcript1, transcript2, |
|
| 42 |
#' names_to_exclude = c("dead_air", "silence")
|
|
| 43 |
#' ) |
|
| 44 |
calculate_content_similarity <- function( |
|
| 45 |
transcript1 = NULL, |
|
| 46 |
transcript2 = NULL, |
|
| 47 |
names_to_exclude = c("dead_air")) {
|
|
| 48 |
# DEPRECATED: This function will be removed in the next version |
|
| 49 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 50 | 27x |
warning("Function 'calculate_content_similarity' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 51 | ||
| 52 |
# Handle NULL transcripts |
|
| 53 | 27x |
if (is.null(transcript1) || is.null(transcript2)) {
|
| 54 | 7x |
return(0.0) |
| 55 |
} |
|
| 56 | ||
| 57 |
# Filter out excluded names (only if name column exists) |
|
| 58 | 20x |
if (!is.null(names_to_exclude) && |
| 59 | 20x |
"name" %in% names(transcript1) && |
| 60 | 20x |
"name" %in% names(transcript2)) {
|
| 61 |
# Use base R filtering instead of dplyr to avoid segmentation fault |
|
| 62 | 17x |
transcript1 <- transcript1[!transcript1$name %in% names_to_exclude, , drop = FALSE] |
| 63 | 17x |
transcript2 <- transcript2[!transcript2$name %in% names_to_exclude, , drop = FALSE] |
| 64 |
} |
|
| 65 | ||
| 66 |
# If either transcript is empty after filtering, return 0 |
|
| 67 | 20x |
if (nrow(transcript1) == 0 || nrow(transcript2) == 0) {
|
| 68 | 2x |
return(0.0) |
| 69 |
} |
|
| 70 | ||
| 71 |
# Calculate similarity metrics |
|
| 72 | ||
| 73 |
# 1. Speaker similarity (proportion of speakers in common) |
|
| 74 | 18x |
speaker_sim <- 0.0 |
| 75 | 18x |
if ("name" %in% names(transcript1) && "name" %in% names(transcript2)) {
|
| 76 | 17x |
speakers1 <- unique(transcript1$name) |
| 77 | 17x |
speakers2 <- unique(transcript2$name) |
| 78 | 17x |
speaker_sim <- length(intersect(speakers1, speakers2)) / length(union(speakers1, speakers2)) |
| 79 |
} |
|
| 80 | ||
| 81 |
# 2. Duration similarity |
|
| 82 | 18x |
duration_sim <- 0.0 |
| 83 | 18x |
if ("duration" %in% names(transcript1) && "duration" %in% names(transcript2)) {
|
| 84 |
# Convert difftime to numeric seconds if needed |
|
| 85 | 17x |
duration1_numeric <- if (inherits(transcript1$duration, "difftime")) {
|
| 86 | 11x |
as.numeric(transcript1$duration, units = "secs") |
| 87 |
} else {
|
|
| 88 | 6x |
transcript1$duration |
| 89 |
} |
|
| 90 | 17x |
duration2_numeric <- if (inherits(transcript2$duration, "difftime")) {
|
| 91 | 11x |
as.numeric(transcript2$duration, units = "secs") |
| 92 |
} else {
|
|
| 93 | 6x |
transcript2$duration |
| 94 |
} |
|
| 95 | ||
| 96 | 17x |
total_duration1 <- sum(duration1_numeric, na.rm = TRUE) |
| 97 | 17x |
total_duration2 <- sum(duration2_numeric, na.rm = TRUE) |
| 98 | 17x |
if (total_duration1 > 0 || total_duration2 > 0) {
|
| 99 | 16x |
duration_sim <- 1 - abs(total_duration1 - total_duration2) / max(total_duration1, total_duration2) |
| 100 |
} |
|
| 101 |
} |
|
| 102 | ||
| 103 |
# 3. Word count similarity |
|
| 104 | 18x |
word_sim <- 0.0 |
| 105 | 18x |
if ("wordcount" %in% names(transcript1) && "wordcount" %in% names(transcript2)) {
|
| 106 | 17x |
total_words1 <- sum(transcript1$wordcount, na.rm = TRUE) |
| 107 | 17x |
total_words2 <- sum(transcript2$wordcount, na.rm = TRUE) |
| 108 | 17x |
if (total_words1 > 0 || total_words2 > 0) {
|
| 109 | 16x |
word_sim <- 1 - abs(total_words1 - total_words2) / max(total_words1, total_words2) |
| 110 |
} |
|
| 111 |
} |
|
| 112 | ||
| 113 |
# 4. Comment count similarity |
|
| 114 | 18x |
comment_sim <- 0.0 |
| 115 | 18x |
if (nrow(transcript1) > 0 && nrow(transcript2) > 0) {
|
| 116 | 18x |
comment_sim <- 1 - abs(nrow(transcript1) - nrow(transcript2)) / max(nrow(transcript1), nrow(transcript2)) |
| 117 | ! |
} else if (nrow(transcript1) == 0 && nrow(transcript2) == 0) {
|
| 118 | ! |
comment_sim <- 1.0 # Both empty = identical |
| 119 |
} |
|
| 120 | ||
| 121 |
# Check if we have any meaningful similarity metrics |
|
| 122 | 18x |
has_meaningful_data <- (speaker_sim > 0 || duration_sim > 0 || word_sim > 0) |
| 123 | ||
| 124 |
# Combine similarities (weighted average) |
|
| 125 | 18x |
if (has_meaningful_data) {
|
| 126 | 16x |
overall_sim <- (speaker_sim * 0.3 + duration_sim * 0.3 + word_sim * 0.2 + comment_sim * 0.2) |
| 127 |
} else {
|
|
| 128 |
# If no meaningful data, return 0.0 (as expected by tests) |
|
| 129 | 2x |
overall_sim <- 0.0 |
| 130 |
} |
|
| 131 | ||
| 132 | 18x |
return(overall_sim) |
| 133 |
} |
| 1 |
#' Consolidate Transcript |
|
| 2 |
#' |
|
| 3 |
#' Take a tibble containing the comments from a Zoom recording transcript and return a tibble that consolidates all consecutive comments from the same speaker where the time between the end of the first comment and start of the second comment is less than `max_pause_sec` seconds. This function addresses an issue with the Zoom transcript where the speaker is speaking a continuous sentence, but the Zoom transcript will cut the comment into two lines. |
|
| 4 |
#' For example, a comment of "This should be a single sentence." is often split into "This should be" and "a single sentence". This function stitches those together into "This should be a single sentence." where the `start` time of the consolidated comment will be the beginning of the first row and the `end` time of the consolidated comment will be the ending of the last row. |
|
| 5 |
#' |
|
| 6 |
#' @importFrom stats aggregate setNames |
|
| 7 |
#' |
|
| 8 |
#' @param df A tibble containing the comments from a Zoom recording transcript. |
|
| 9 |
#' @param max_pause_sec Maximum pause between comments to be consolidated. If |
|
| 10 |
#' the raw comments from the Zoom recording transcript contain 2 consecutive |
|
| 11 |
#' comments from the same speaker, and the time between the end of the first |
|
| 12 |
#' comment and start of the second comment is less than `max_pause_sec` |
|
| 13 |
#' seconds, then the comments will be consolidated. If the time between the |
|
| 14 |
#' comments is larger, they will not be consolidated. Defaults to 1. |
|
| 15 |
#' |
|
| 16 |
#' @return A tibble containing consolidated comments from a Zoom recording |
|
| 17 |
#' transcript. |
|
| 18 |
#' @export |
|
| 19 |
#' |
|
| 20 |
#' @examples |
|
| 21 |
#' consolidate_transcript(df = "NULL") |
|
| 22 |
#' |
|
| 23 |
consolidate_transcript <- function(df = NULL, max_pause_sec = 1) {
|
|
| 24 |
. <- |
|
| 25 | 255x |
begin <- |
| 26 | 255x |
comment <- |
| 27 | 255x |
comment_num <- |
| 28 | 255x |
duration <- |
| 29 | 255x |
end <- |
| 30 | 255x |
name <- |
| 31 | 255x |
name_flag <- |
| 32 | 255x |
prev_end <- |
| 33 | 255x |
prior_dead_air <- |
| 34 | 255x |
start <- |
| 35 | 255x |
time_flag <- timestamp <- wordcount <- prior_speaker <- transcript_file <- NULL |
| 36 | ||
| 37 | 255x |
if (tibble::is_tibble(df)) {
|
| 38 |
# Handle empty data case |
|
| 39 | 255x |
if (nrow(df) == 0) {
|
| 40 |
# Return empty tibble with correct structure |
|
| 41 | ! |
result_cols <- c("name", "comment", "start", "end", "duration", "wordcount")
|
| 42 | ! |
if ("transcript_file" %in% names(df)) {
|
| 43 | ! |
result_cols <- c("transcript_file", result_cols)
|
| 44 |
} |
|
| 45 | ||
| 46 | ! |
empty_result <- stats::setNames( |
| 47 | ! |
lapply(result_cols, function(x) if (x %in% c("duration", "wordcount")) numeric(0) else character(0)),
|
| 48 | ! |
result_cols |
| 49 |
) |
|
| 50 | ! |
return(tibble::as_tibble(empty_result)) |
| 51 |
} |
|
| 52 | ||
| 53 |
# Ensure time columns are of type hms (replacing lubridate::period to avoid segfaults) |
|
| 54 |
# Use base R operations to avoid dplyr segfaults |
|
| 55 | 255x |
df$start <- hms::as_hms(df$start) |
| 56 | 255x |
df$end <- hms::as_hms(df$end) |
| 57 | ||
| 58 |
# Use base R operations to avoid segmentation faults with dplyr + hms |
|
| 59 |
# Sort by start time for lag operations |
|
| 60 | 255x |
df <- df[order(df$start), ] |
| 61 | ||
| 62 |
# Calculate lag values using base R |
|
| 63 | 255x |
df$prev_end <- c(hms::hms(0), df$end[-length(df$end)]) |
| 64 | 255x |
df$prior_dead_air <- as.numeric(df$start - df$prev_end) |
| 65 | 255x |
df$prior_speaker <- c(df$name[1], df$name[-length(df$name)]) |
| 66 | ||
| 67 |
# Calculate flags |
|
| 68 | 255x |
df$name_flag <- ((df$name != df$prior_speaker) | is.na(df$name) | is.na(df$prior_speaker)) |
| 69 | 255x |
df$time_flag <- df$prior_dead_air > max_pause_sec |
| 70 | 255x |
df$comment_num <- cumsum(df$name_flag | df$time_flag) |
| 71 | ||
| 72 |
# Highly optimized aggregation using vectorized operations |
|
| 73 |
# Use aggregate() for efficient grouping operations |
|
| 74 | 255x |
if ("transcript_file" %in% names(df)) {
|
| 75 |
# Group by both transcript_file and comment_num |
|
| 76 | 255x |
agg_result <- stats::aggregate( |
| 77 | 255x |
list( |
| 78 | 255x |
name = df$name, |
| 79 | 255x |
comment = df$comment, |
| 80 | 255x |
start = df$start, |
| 81 | 255x |
end = df$end |
| 82 |
), |
|
| 83 | 255x |
by = list( |
| 84 | 255x |
transcript_file = df$transcript_file, |
| 85 | 255x |
comment_num = df$comment_num |
| 86 |
), |
|
| 87 | 255x |
FUN = function(x) {
|
| 88 | 9444x |
if (length(x) == 1) {
|
| 89 | 7128x |
return(x) |
| 90 |
} |
|
| 91 |
# For comments, paste them together |
|
| 92 | 2316x |
if (is.character(x) && all(sapply(x, is.character))) {
|
| 93 | 1158x |
return(paste(x, collapse = " ")) |
| 94 |
} |
|
| 95 |
# For other columns, take first/last as appropriate |
|
| 96 | 1158x |
if (is.character(x) || is.numeric(x)) {
|
| 97 | ! |
return(x[1]) # Take first for name, start |
| 98 |
} |
|
| 99 |
# For end times, take the last one |
|
| 100 | 1158x |
return(x[length(x)]) |
| 101 |
}, |
|
| 102 | 255x |
simplify = FALSE |
| 103 |
) |
|
| 104 | ||
| 105 |
# Extract the aggregated values |
|
| 106 | 255x |
result <- data.frame( |
| 107 | 255x |
transcript_file = agg_result$transcript_file, |
| 108 | 255x |
name = unlist(agg_result$name), |
| 109 | 255x |
comment = unlist(agg_result$comment), |
| 110 | 255x |
start = unlist(agg_result$start), |
| 111 | 255x |
end = unlist(agg_result$end), |
| 112 | 255x |
stringsAsFactors = FALSE |
| 113 |
) |
|
| 114 |
} else {
|
|
| 115 |
# Group by comment_num only |
|
| 116 | ! |
agg_result <- stats::aggregate( |
| 117 | ! |
list( |
| 118 | ! |
name = df$name, |
| 119 | ! |
comment = df$comment, |
| 120 | ! |
start = df$start, |
| 121 | ! |
end = df$end |
| 122 |
), |
|
| 123 | ! |
by = list(comment_num = df$comment_num), |
| 124 | ! |
FUN = function(x) {
|
| 125 | ! |
if (length(x) == 1) {
|
| 126 | ! |
return(x) |
| 127 |
} |
|
| 128 |
# For comments, paste them together |
|
| 129 | ! |
if (is.character(x) && all(sapply(x, is.character))) {
|
| 130 | ! |
return(paste(x, collapse = " ")) |
| 131 |
} |
|
| 132 |
# For other columns, take first/last as appropriate |
|
| 133 | ! |
if (is.character(x) || is.numeric(x)) {
|
| 134 | ! |
return(x[1]) # Take first for name, start |
| 135 |
} |
|
| 136 |
# For end times, take the last one |
|
| 137 | ! |
return(x[length(x)]) |
| 138 |
}, |
|
| 139 | ! |
simplify = FALSE |
| 140 |
) |
|
| 141 | ||
| 142 |
# Extract the aggregated values |
|
| 143 | ! |
result <- data.frame( |
| 144 | ! |
name = unlist(agg_result$name), |
| 145 | ! |
comment = unlist(agg_result$comment), |
| 146 | ! |
start = unlist(agg_result$start), |
| 147 | ! |
end = unlist(agg_result$end), |
| 148 | ! |
stringsAsFactors = FALSE |
| 149 |
) |
|
| 150 |
} |
|
| 151 | ||
| 152 |
# Calculate duration and wordcount efficiently |
|
| 153 | 255x |
result$duration <- as.numeric(result$end - result$start) |
| 154 | ||
| 155 |
# Vectorized wordcount calculation |
|
| 156 | 255x |
result$wordcount <- vapply( |
| 157 | 255x |
strsplit(result$comment, "\\s+"), |
| 158 | 255x |
function(x) length(x[x != ""]), |
| 159 | 255x |
integer(1) |
| 160 |
) |
|
| 161 | ||
| 162 |
# Convert to tibble to maintain expected return type |
|
| 163 | 255x |
return(tibble::as_tibble(result)) |
| 164 |
} |
|
| 165 |
} |
| 1 |
#' Set Privacy Defaults |
|
| 2 |
#' |
|
| 3 |
#' Configure global privacy behavior for the package. The default on package |
|
| 4 |
#' load is `mask`, which replaces personally identifiable fields with |
|
| 5 |
#' FERPA-safe placeholders. Set to `"none"` to disable masking (not |
|
| 6 |
#' recommended). |
|
| 7 |
#' |
|
| 8 |
#' @param privacy_level One of `c("ferpa_strict", "ferpa_standard", "mask", "none")`.
|
|
| 9 |
#' Defaults to `"mask"`. Use `"ferpa_strict"` for maximum FERPA compliance. |
|
| 10 |
#' @param unmatched_names_action Action to take when unmatched names are found. |
|
| 11 |
#' One of `c("stop", "warn")`. Defaults to `"stop"` for maximum privacy protection.
|
|
| 12 |
#' Use `"warn"` for guided matching with user intervention. |
|
| 13 |
#' |
|
| 14 |
#' @return Invisibly returns a list with the chosen privacy level and unmatched names action. |
|
| 15 |
#' |
|
| 16 |
#' @seealso [ensure_privacy()], [safe_name_matching_workflow()] |
|
| 17 |
#' @export |
|
| 18 |
#' |
|
| 19 |
#' @examples |
|
| 20 |
#' # Set privacy to mask (default) |
|
| 21 |
#' set_privacy_defaults("mask")
|
|
| 22 |
#' |
|
| 23 |
#' # Set FERPA standard compliance |
|
| 24 |
#' set_privacy_defaults("ferpa_standard")
|
|
| 25 |
#' |
|
| 26 |
#' # Set maximum FERPA compliance |
|
| 27 |
#' set_privacy_defaults("ferpa_strict")
|
|
| 28 |
#' |
|
| 29 |
#' # Temporarily disable masking (will emit a warning) |
|
| 30 |
#' set_privacy_defaults("none")
|
|
| 31 |
#' |
|
| 32 |
#' # Configure unmatched names behavior |
|
| 33 |
#' set_privacy_defaults( |
|
| 34 |
#' privacy_level = "mask", |
|
| 35 |
#' unmatched_names_action = "warn" |
|
| 36 |
#' ) |
|
| 37 |
set_privacy_defaults <- function(privacy_level = c("ferpa_strict", "ferpa_standard", "mask", "none"),
|
|
| 38 |
unmatched_names_action = c("stop", "warn")) {
|
|
| 39 | 15x |
privacy_level <- match.arg(privacy_level) |
| 40 | 15x |
unmatched_names_action <- match.arg(unmatched_names_action) |
| 41 | ||
| 42 |
# Validate privacy level |
|
| 43 | 15x |
if (identical(privacy_level, "none")) {
|
| 44 | 6x |
warning( |
| 45 | 6x |
"Privacy disabled globally; outputs may contain identifiable data.", |
| 46 | 6x |
call. = FALSE |
| 47 |
) |
|
| 48 | 9x |
} else if (identical(privacy_level, "ferpa_strict")) {
|
| 49 | 3x |
diag_message( |
| 50 | 3x |
"FERPA strict mode enabled; maximum privacy protection applied." |
| 51 |
) |
|
| 52 | 6x |
} else if (identical(privacy_level, "ferpa_standard")) {
|
| 53 | 2x |
diag_message( |
| 54 | 2x |
"FERPA standard mode enabled; educational compliance protection applied." |
| 55 |
) |
|
| 56 |
} |
|
| 57 | ||
| 58 |
# Validate unmatched names action |
|
| 59 | 15x |
if (identical(unmatched_names_action, "stop")) {
|
| 60 | 13x |
diag_message( |
| 61 | 13x |
"Unmatched names action set to 'stop' - maximum privacy protection enabled." |
| 62 |
) |
|
| 63 | 2x |
} else if (identical(unmatched_names_action, "warn")) {
|
| 64 | 2x |
diag_message( |
| 65 | 2x |
"Unmatched names action set to 'warn' - guided matching enabled." |
| 66 |
) |
|
| 67 |
} |
|
| 68 | ||
| 69 |
# Set global options |
|
| 70 | 15x |
options( |
| 71 | 15x |
zoomstudentengagement.privacy_level = privacy_level, |
| 72 | 15x |
zoomstudentengagement.unmatched_names_action = unmatched_names_action |
| 73 |
) |
|
| 74 | ||
| 75 |
# Return configuration invisibly |
|
| 76 | 15x |
invisible(list( |
| 77 | 15x |
privacy_level = privacy_level, |
| 78 | 15x |
unmatched_names_action = unmatched_names_action |
| 79 |
)) |
|
| 80 |
} |
| 1 |
#' Make Transcripts Summary |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from summary metrics by student and class |
|
| 4 |
#' session (`transcripts_session_summary_df`) and summarizes results at the |
|
| 5 |
#' level of the class section and preferred student name. |
|
| 6 |
#' |
|
| 7 |
#' |
|
| 8 |
#' @param transcripts_session_summary_df a tibble containing session details and |
|
| 9 |
#' summary metrics by speaker for all class sessions (and placeholders for |
|
| 10 |
#' missing sections), including customized student names, and summarizes |
|
| 11 |
#' results at the level of the session and preferred student name. |
|
| 12 |
#' |
|
| 13 |
#' @return A tibble that summarizes results at the level of the class section and preferred student name |
|
| 14 |
#' @export |
|
| 15 |
#' |
|
| 16 |
#' @examples |
|
| 17 |
#' # Load required packages |
|
| 18 |
#' library(dplyr) |
|
| 19 |
#' |
|
| 20 |
#' # Create a simple sample data frame for testing |
|
| 21 |
#' sample_data <- tibble::tibble( |
|
| 22 |
#' section = c("A", "A", "B"),
|
|
| 23 |
#' preferred_name = c("John Smith", "Jane Doe", "Bob Wilson"),
|
|
| 24 |
#' n = c(5, 3, 2), |
|
| 25 |
#' duration = c(300, 180, 120), |
|
| 26 |
#' wordcount = c(500, 300, 200) |
|
| 27 |
#' ) |
|
| 28 |
#' |
|
| 29 |
#' # Test the function with the sample data |
|
| 30 |
#' make_transcripts_summary_df(sample_data) |
|
| 31 |
make_transcripts_summary_df <- |
|
| 32 |
function(transcripts_session_summary_df = NULL) {
|
|
| 33 | 37x |
duration <- n <- preferred_name <- section <- wordcount <- NULL |
| 34 | ||
| 35 | 37x |
if (tibble::is_tibble(transcripts_session_summary_df) |
| 36 |
) {
|
|
| 37 |
# Check for empty input |
|
| 38 | 31x |
if (nrow(transcripts_session_summary_df) == 0) {
|
| 39 | 4x |
return(tibble::tibble( |
| 40 | 4x |
section = character(), |
| 41 | 4x |
preferred_name = character(), |
| 42 | 4x |
session_ct = integer(), |
| 43 | 4x |
n = numeric(), |
| 44 | 4x |
duration = numeric(), |
| 45 | 4x |
wordcount = numeric(), |
| 46 | 4x |
wpm = numeric(), |
| 47 | 4x |
perc_n = numeric(), |
| 48 | 4x |
perc_duration = numeric(), |
| 49 | 4x |
perc_wordcount = numeric() |
| 50 |
)) |
|
| 51 |
} |
|
| 52 | ||
| 53 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 54 |
# Group by section and preferred_name |
|
| 55 | 27x |
group_cols <- c("section", "preferred_name")
|
| 56 | ||
| 57 |
# Create a unique identifier for each group |
|
| 58 | 27x |
transcripts_session_summary_df$group_id <- apply(transcripts_session_summary_df[, group_cols], 1, paste, collapse = "|") |
| 59 | ||
| 60 |
# Aggregate by group using base R |
|
| 61 | 27x |
group_ids <- unique(transcripts_session_summary_df$group_id) |
| 62 | 27x |
result_rows <- list() |
| 63 | ||
| 64 | 27x |
for (i in seq_along(group_ids)) {
|
| 65 | 63x |
group_id <- group_ids[i] |
| 66 | 63x |
group_data <- transcripts_session_summary_df[transcripts_session_summary_df$group_id == group_id, , drop = FALSE] |
| 67 | ||
| 68 |
# Calculate summaries |
|
| 69 | 63x |
session_ct <- sum(!is.na(group_data$duration)) |
| 70 | 63x |
n_sum <- sum(group_data$n, na.rm = TRUE) |
| 71 | 63x |
duration_sum <- sum(group_data$duration, na.rm = TRUE) |
| 72 | 63x |
wordcount_sum <- sum(group_data$wordcount, na.rm = TRUE) |
| 73 | ||
| 74 |
# Get group identifiers |
|
| 75 | 63x |
group_parts <- strsplit(group_id, "\\|")[[1]] |
| 76 | ||
| 77 | 63x |
result_rows[[i]] <- data.frame( |
| 78 | 63x |
section = group_parts[1], |
| 79 | 63x |
preferred_name = group_parts[2], |
| 80 | 63x |
session_ct = session_ct, |
| 81 | 63x |
n = n_sum, |
| 82 | 63x |
duration = duration_sum, |
| 83 | 63x |
wordcount = wordcount_sum, |
| 84 | 63x |
stringsAsFactors = FALSE |
| 85 |
) |
|
| 86 |
} |
|
| 87 | ||
| 88 |
# Combine results |
|
| 89 | 27x |
result <- do.call(rbind, result_rows) |
| 90 | ||
| 91 |
# Check if result is empty |
|
| 92 | 27x |
if (nrow(result) == 0) {
|
| 93 | ! |
return(tibble::tibble( |
| 94 | ! |
section = character(), |
| 95 | ! |
preferred_name = character(), |
| 96 | ! |
session_ct = integer(), |
| 97 | ! |
n = numeric(), |
| 98 | ! |
duration = numeric(), |
| 99 | ! |
wordcount = numeric(), |
| 100 | ! |
wpm = numeric(), |
| 101 | ! |
perc_n = numeric(), |
| 102 | ! |
perc_duration = numeric(), |
| 103 | ! |
perc_wordcount = numeric() |
| 104 |
)) |
|
| 105 |
} |
|
| 106 | ||
| 107 |
# Calculate percentages by section using base R |
|
| 108 | 27x |
sections <- unique(result$section) |
| 109 | 27x |
final_rows <- list() |
| 110 | ||
| 111 | 27x |
for (i in seq_along(sections)) {
|
| 112 | 34x |
section_data <- result[result$section == sections[i], , drop = FALSE] |
| 113 | ||
| 114 |
# Calculate percentages |
|
| 115 | 34x |
total_n <- sum(section_data$n, na.rm = TRUE) |
| 116 | 34x |
total_duration <- sum(section_data$duration, na.rm = TRUE) |
| 117 | 34x |
total_wordcount <- sum(section_data$wordcount, na.rm = TRUE) |
| 118 | ||
| 119 | 34x |
section_data$wpm <- section_data$wordcount / section_data$duration |
| 120 | 34x |
section_data$perc_n <- section_data$n / total_n * 100 |
| 121 | 34x |
section_data$perc_duration <- section_data$duration / total_duration * 100 |
| 122 | 34x |
section_data$perc_wordcount <- section_data$wordcount / total_wordcount * 100 |
| 123 | ||
| 124 | 34x |
final_rows[[i]] <- section_data |
| 125 |
} |
|
| 126 | ||
| 127 |
# Combine final results |
|
| 128 | 27x |
final_result <- do.call(rbind, final_rows) |
| 129 | ||
| 130 |
# Check if final_result is empty before sorting |
|
| 131 | 27x |
if (nrow(final_result) == 0) {
|
| 132 | ! |
return(tibble::tibble( |
| 133 | ! |
section = character(), |
| 134 | ! |
preferred_name = character(), |
| 135 | ! |
session_ct = integer(), |
| 136 | ! |
n = numeric(), |
| 137 | ! |
duration = numeric(), |
| 138 | ! |
wordcount = numeric(), |
| 139 | ! |
wpm = numeric(), |
| 140 | ! |
perc_n = numeric(), |
| 141 | ! |
perc_duration = numeric(), |
| 142 | ! |
perc_wordcount = numeric() |
| 143 |
)) |
|
| 144 |
} |
|
| 145 | ||
| 146 |
# Sort by duration (descending) using base R |
|
| 147 | 27x |
final_result <- final_result[order(-final_result$duration), , drop = FALSE] |
| 148 | ||
| 149 |
# Convert to tibble to maintain expected return type |
|
| 150 | 27x |
return(tibble::as_tibble(final_result)) |
| 151 |
} |
|
| 152 |
} |
| 1 |
#' Load Zoom Recording Transcript Files List |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from a provided folder including |
|
| 4 |
#' transcript files of Zoom recordings. |
|
| 5 |
#' |
|
| 6 |
#' ## Download Transcripts |
|
| 7 |
#' 1. Go to [https://www.zoom.us/recording](https://www.zoom.us/recording) |
|
| 8 |
#' 2. Click on each individual record to go to the page for that recording |
|
| 9 |
#' 3. Download the Audio Transcript and Chat File for each |
|
| 10 |
#' - Chat: `GMT\\d{8}-\\d{6}_Recording.cc.vtt`
|
|
| 11 |
#' - Transcript: `GMT\\d{8}-\\d{6}_Recording.transcript.vtt`
|
|
| 12 |
#' 4. Copy the Audio Transcript and Chat Files to `data/transcripts/` |
|
| 13 |
#' (or whatever path you identify in the `data_folder` and |
|
| 14 |
#' `transcripts_folder` parameters). |
|
| 15 |
#' |
|
| 16 |
#' @param data_folder Overall data folder for your recordings and data. Defaults |
|
| 17 |
#' to `data` |
|
| 18 |
#' @param transcripts_folder specific subfolder of the data folder where you |
|
| 19 |
#' will store the cloud recording csvs and transcripts |
|
| 20 |
#' @param transcript_files_names_pattern REGEX pattern used to match the |
|
| 21 |
#' transcript file names. Defaults to `GMT\\d{8}-\\d{6}_Recording`
|
|
| 22 |
#' @param dt_extract_pattern REGEX pattern used to extract the date of the |
|
| 23 |
#' transcript from the transcript file name. Defaults to `(?<=GMT)\\d{8}`
|
|
| 24 |
#' @param transcript_file_extension_pattern REGEX pattern used to identify |
|
| 25 |
#' transcript files (as opposed to chat or closed caption files). Defaults to |
|
| 26 |
#' `.transcript` |
|
| 27 |
#' @param closed_caption_file_extension_pattern REGEX pattern used to identify |
|
| 28 |
#' closed caption files (as opposed to chat or transcript files). Defaults to |
|
| 29 |
#' `.cc` |
|
| 30 |
#' @param recording_start_pattern REGEX pattern used to extract the recording |
|
| 31 |
#' start time of the transcript from the transcript file name. Defaults to |
|
| 32 |
#' `(?<=GMT)\\d{8}-\\d{6}`
|
|
| 33 |
#' @param recording_start_format Pattern used to parse the format of the |
|
| 34 |
#' recording start time of the transcript. Defaults to `\%Y\%m\%d-\%H\%M\%S` |
|
| 35 |
#' @param start_time_local_tzone Local time zone of the recording start time of |
|
| 36 |
#' the transcript. Defaults to `America/Los_Angeles` |
|
| 37 |
#' |
|
| 38 |
#' @return A tibble listing the transcript files from the zoom recordings |
|
| 39 |
#' loaded from the cloud recording csvs and transcripts. |
|
| 40 |
#' @export |
|
| 41 |
#' |
|
| 42 |
#' @examples |
|
| 43 |
#' load_transcript_files_list() |
|
| 44 |
load_transcript_files_list <- |
|
| 45 |
function(data_folder = ".", |
|
| 46 |
transcripts_folder = "transcripts", |
|
| 47 |
# zoom_recorded_sessions_csv_names_pattern = |
|
| 48 |
# 'zoomus_recordings__\\d{8}(?:\\s+copy\\s*\\d*)?\\.csv',
|
|
| 49 |
transcript_files_names_pattern = |
|
| 50 |
"GMT\\d{8}-\\d{6}_Recording",
|
|
| 51 |
dt_extract_pattern = "(?<=GMT)\\d{8}",
|
|
| 52 |
transcript_file_extension_pattern = ".transcript", |
|
| 53 |
closed_caption_file_extension_pattern = ".cc", |
|
| 54 |
recording_start_pattern = "(?<=GMT)\\d{8}-\\d{6}",
|
|
| 55 |
recording_start_format = "%Y%m%d-%H%M%S", |
|
| 56 |
start_time_local_tzone = "America/Los_Angeles") {
|
|
| 57 | 5x |
. <- file_name <- recording_start <- file_type <- NULL |
| 58 | ||
| 59 | 5x |
transcripts_folder_path <- file.path(data_folder, transcripts_folder) |
| 60 | ||
| 61 | 5x |
if (!dir.exists(transcripts_folder_path)) {
|
| 62 | 1x |
return(NULL) |
| 63 |
} |
|
| 64 | ||
| 65 | 4x |
transcript_files <- list.files( |
| 66 | 4x |
transcripts_folder_path, |
| 67 | 4x |
transcript_files_names_pattern |
| 68 |
) |
|
| 69 | ||
| 70 |
# Return empty tibble when no matching files are found |
|
| 71 | 4x |
if (length(transcript_files) == 0) {
|
| 72 | 3x |
return(tibble::as_tibble(data.frame())) |
| 73 |
} |
|
| 74 | ||
| 75 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 76 |
# Create data frame with file names |
|
| 77 | 1x |
df <- data.frame(file_name = transcript_files, stringsAsFactors = FALSE) |
| 78 | ||
| 79 |
# Extract date |
|
| 80 | 1x |
df$date_extract <- stringr::str_extract(df$file_name, dt_extract_pattern) |
| 81 | ||
| 82 |
# Determine file type |
|
| 83 | 1x |
df$file_type <- ifelse( |
| 84 | 1x |
grepl(transcript_file_extension_pattern, df$file_name, fixed = FALSE), |
| 85 | 1x |
"transcript_file", |
| 86 | 1x |
ifelse( |
| 87 | 1x |
grepl(closed_caption_file_extension_pattern, df$file_name, fixed = FALSE), |
| 88 | 1x |
"closed_caption_file", |
| 89 | 1x |
"chat_file" |
| 90 |
) |
|
| 91 |
) |
|
| 92 | ||
| 93 |
# Extract and parse recording start time |
|
| 94 | 1x |
recording_start_str <- stringr::str_extract(df$file_name, recording_start_pattern) |
| 95 | 1x |
df$recording_start <- lubridate::parse_date_time(recording_start_str, orders = recording_start_format) |
| 96 | 1x |
df$recording_start <- as.POSIXct(df$recording_start, tz = "UTC") |
| 97 | 1x |
df$start_time_local <- lubridate::with_tz(df$recording_start, tzone = start_time_local_tzone) |
| 98 | ||
| 99 |
# Pivot to wide format per recording using base R |
|
| 100 | 1x |
groups_df <- unique(df[, c("date_extract", "recording_start", "start_time_local"), drop = FALSE])
|
| 101 |
# Ensure groups are ordered by time |
|
| 102 | 1x |
groups_df <- groups_df[order(groups_df$start_time_local), , drop = FALSE] |
| 103 |
# Initialize result with groups |
|
| 104 | 1x |
result <- groups_df |
| 105 | ||
| 106 |
# Add file type columns per group |
|
| 107 | 1x |
file_types <- unique(df$file_type) |
| 108 | 1x |
for (file_type in file_types) {
|
| 109 | 3x |
result[[file_type]] <- NA_character_ |
| 110 |
} |
|
| 111 | ||
| 112 |
# Fill in file names per group and type |
|
| 113 | 1x |
if (nrow(result) > 0) {
|
| 114 | 1x |
for (k in seq_len(nrow(result))) {
|
| 115 | 1x |
row_date <- result$date_extract[k] |
| 116 | 1x |
row_start <- result$recording_start[k] |
| 117 | 1x |
for (file_type in file_types) {
|
| 118 | 3x |
type_files <- df[df$file_type == file_type & df$date_extract == row_date & df$recording_start == row_start, "file_name", drop = TRUE] |
| 119 | 3x |
if (length(type_files) > 0) {
|
| 120 | 3x |
result[[file_type]][k] <- type_files[1] |
| 121 |
} |
|
| 122 |
} |
|
| 123 |
} |
|
| 124 |
} |
|
| 125 | ||
| 126 | 1x |
return(tibble::as_tibble(result)) |
| 127 |
} |
| 1 |
#' Load Zoom Transcript |
|
| 2 |
#' |
|
| 3 |
#' Load a Zoom recording transcript and return tibble containing the comments from a Zoom recording transcript |
|
| 4 |
#' |
|
| 5 |
#' Original code posted by Conor Healy: |
|
| 6 |
#' https://ucbischool.slack.com/archives/C02A36407K9/p1631855705002000 Addition |
|
| 7 |
#' of `wordcount` by Brooks Ambrose: |
|
| 8 |
#' https://gist.github.com/brooksambrose/1a8a673eb3bf884c1868ad4d80f08246 |
|
| 9 | ||
| 10 | ||
| 11 | ||
| 12 | ||
| 13 |
#' @param transcript_file_path File path of a .transcript.vtt file of a Zoom recording |
|
| 14 |
#' transcript. |
|
| 15 |
#' |
|
| 16 |
#' @return A tibble containing the comments from a Zoom recording |
|
| 17 |
#' transcript, or NULL if the file is empty |
|
| 18 |
#' @export |
|
| 19 |
#' |
|
| 20 |
#' @examples |
|
| 21 |
#' # Load a sample transcript from the package's extdata directory |
|
| 22 |
#' transcript_file <- system.file("extdata/transcripts/GMT20240124-202901_Recording.transcript.vtt",
|
|
| 23 |
#' package = "zoomstudentengagement" |
|
| 24 |
#' ) |
|
| 25 |
#' load_zoom_transcript(transcript_file_path = transcript_file) |
|
| 26 |
#' |
|
| 27 |
load_zoom_transcript <- function(transcript_file_path = NULL) {
|
|
| 28 |
. <- |
|
| 29 | 459x |
begin <- |
| 30 | 459x |
comment_num <- |
| 31 | 459x |
duration <- |
| 32 | 459x |
end <- |
| 33 | 459x |
name <- |
| 34 | 459x |
prior_dead_air <- start <- timestamp <- wordcount <- prior_speaker <- NULL |
| 35 | ||
| 36 | 459x |
if (!file.exists(transcript_file_path)) {
|
| 37 | 2x |
abort_zse("file.exists(transcript_file_path) is not TRUE", class = "zse_input_error")
|
| 38 |
} |
|
| 39 | ||
| 40 |
# Read the first line to validate VTT format |
|
| 41 | 457x |
first_line <- readLines(transcript_file_path, n = 1) |
| 42 | 457x |
if (first_line != "WEBVTT") {
|
| 43 | 4x |
abort_zse(paste0("Invalid VTT: expected 'WEBVTT', got '", first_line, "'"), class = "zse_input_error")
|
| 44 |
} |
|
| 45 | ||
| 46 | 453x |
transcript_file <- basename(transcript_file_path) |
| 47 | ||
| 48 |
# Read the transcript file with explicit column specification to avoid warnings |
|
| 49 | 453x |
transcript_vtt <- readr::read_tsv( |
| 50 | 453x |
transcript_file_path, |
| 51 | 453x |
col_names = "WEBVTT", |
| 52 | 453x |
skip = 1, # Skip the "WEBVTT" header row |
| 53 | 453x |
show_col_types = FALSE |
| 54 |
) |
|
| 55 | ||
| 56 |
# Return NULL for empty files |
|
| 57 | 453x |
if (nrow(transcript_vtt) == 0) {
|
| 58 | 1x |
return(NULL) |
| 59 |
} |
|
| 60 | ||
| 61 |
# Process the transcript |
|
| 62 | 452x |
transcript_cols <- c("comment_num", "timestamp", "comment")
|
| 63 | ||
| 64 |
# Calculate how many complete entries we have |
|
| 65 | 452x |
n_entries <- floor(nrow(transcript_vtt) / 3) |
| 66 | 452x |
if (n_entries == 0) {
|
| 67 | 5x |
return(NULL) |
| 68 |
} |
|
| 69 | ||
| 70 |
# Create a data frame with the correct number of rows |
|
| 71 | 447x |
transcript_df <- tibble::tibble( |
| 72 | 447x |
transcript_file = transcript_file, |
| 73 | 447x |
comment_num = character(n_entries), |
| 74 | 447x |
timestamp = character(n_entries), |
| 75 | 447x |
comment = character(n_entries) |
| 76 |
) |
|
| 77 | ||
| 78 |
# Fill in the data |
|
| 79 | 447x |
for (i in 1:n_entries) {
|
| 80 | 5981x |
start_idx <- (i - 1) * 3 + 1 |
| 81 | 5981x |
transcript_df$comment_num[i] <- transcript_vtt$WEBVTT[start_idx] |
| 82 | 5981x |
transcript_df$timestamp[i] <- transcript_vtt$WEBVTT[start_idx + 1] |
| 83 | 5981x |
transcript_df$comment[i] <- transcript_vtt$WEBVTT[start_idx + 2] |
| 84 |
} |
|
| 85 | ||
| 86 |
# Process the data using base R to avoid segmentation faults |
|
| 87 |
# Split comment into name and text more efficiently |
|
| 88 | 447x |
name_comment_split <- strsplit(transcript_df$comment, ": ", fixed = TRUE) |
| 89 | 447x |
transcript_df$name <- sapply(name_comment_split, function(x) if (length(x) > 1) x[1] else NA_character_) |
| 90 | 447x |
transcript_df$comment <- sapply(name_comment_split, function(x) if (length(x) > 1) paste(x[-1], collapse = ": ") else x[1]) |
| 91 | ||
| 92 |
# Split timestamp more efficiently |
|
| 93 | 447x |
time_split <- strsplit(transcript_df$timestamp, " --> ", fixed = TRUE) |
| 94 | 447x |
transcript_df$start <- sapply(time_split, function(x) if (length(x) == 2) x[1] else NA_character_) |
| 95 | 447x |
transcript_df$end <- sapply(time_split, function(x) if (length(x) == 2) x[2] else NA_character_) |
| 96 | ||
| 97 |
# Convert to hms with error handling and calculate duration |
|
| 98 | 447x |
safe_as_hms <- function(x) {
|
| 99 | 11962x |
tryCatch(hms::as_hms(x), warning = function(w) hms::as_hms(NA), error = function(e) hms::as_hms(NA)) |
| 100 |
} |
|
| 101 | 447x |
transcript_df$start <- do.call(c, lapply(transcript_df$start, safe_as_hms)) |
| 102 | 447x |
transcript_df$end <- do.call(c, lapply(transcript_df$end, safe_as_hms)) |
| 103 | 447x |
transcript_df$duration <- transcript_df$end - transcript_df$start |
| 104 | ||
| 105 |
# Calculate wordcount |
|
| 106 | 447x |
transcript_df$wordcount <- sapply(transcript_df$comment, function(x) {
|
| 107 | 5981x |
if (is.na(x) || x == "") {
|
| 108 | ! |
return(0) |
| 109 |
} |
|
| 110 | 5981x |
length(strsplit(x, " +")[[1]]) |
| 111 |
}) |
|
| 112 | ||
| 113 |
# Select final columns using base R |
|
| 114 | 447x |
result <- transcript_df[, c("transcript_file", "comment_num", "name", "comment", "start", "end", "duration", "wordcount")]
|
| 115 | ||
| 116 |
# Filter out rows with missing or invalid timestamps, comments, or negative duration |
|
| 117 | 447x |
result <- result[ |
| 118 | 447x |
!is.na(result$start) & |
| 119 | 447x |
!is.na(result$end) & |
| 120 | 447x |
!is.na(result$duration) & |
| 121 | 447x |
result$duration >= 0 & |
| 122 | 447x |
!is.na(result$comment) & |
| 123 | 447x |
result$comment != "", , |
| 124 | 447x |
drop = FALSE |
| 125 |
] |
|
| 126 | ||
| 127 | 447x |
if (nrow(result) == 0) {
|
| 128 | 9x |
return(NULL) |
| 129 |
} |
|
| 130 | ||
| 131 |
# Convert to tibble to maintain expected return type and validate minimal shape |
|
| 132 | 438x |
result <- tibble::as_tibble(result) |
| 133 | 438x |
try(validate_schema(result, c("transcript_file", "comment_num", "name", "comment", "start", "end", "duration", "wordcount")), silent = TRUE)
|
| 134 | 438x |
return(result) |
| 135 |
} |
| 1 |
#' Run student summary reports |
|
| 2 |
#' |
|
| 3 |
#' Render the packaged student summary R Markdown template for each student in |
|
| 4 |
#' each section. |
|
| 5 |
#' |
|
| 6 |
#' @param df_sections Tibble with a `section` column listing sections to render. |
|
| 7 |
#' @param df_roster Tibble with columns `section` and `preferred_name`. |
|
| 8 |
#' @param data_folder Directory containing summary CSVs and where reports are written. |
|
| 9 |
#' @param transcripts_session_summary_file Name of the session summary CSV file. |
|
| 10 |
#' @param transcripts_summary_file Name of the summary CSV file. |
|
| 11 |
#' @param student_summary_report Name of the report template file. |
|
| 12 |
#' @param student_summary_report_folder Folder containing the template file. |
|
| 13 |
#' @param output_format Output format passed to [rmarkdown::render()]. |
|
| 14 |
#' |
|
| 15 |
#' @return Invisibly, a character vector of generated report paths. |
|
| 16 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 17 |
#' |
|
| 18 |
#' @examples |
|
| 19 |
#' \dontrun{
|
|
| 20 |
#' run_student_reports( |
|
| 21 |
#' df_sections = tibble::tibble(section = 1), |
|
| 22 |
#' df_roster = tibble::tibble(section = 1, preferred_name = "Alice"), |
|
| 23 |
#' data_folder = tempdir() |
|
| 24 |
#' ) |
|
| 25 |
#' } |
|
| 26 |
run_student_reports <- function( |
|
| 27 |
df_sections, |
|
| 28 |
df_roster, |
|
| 29 |
data_folder, |
|
| 30 |
transcripts_session_summary_file = "transcripts_session_summary.csv", |
|
| 31 |
transcripts_summary_file = "transcripts_summary.csv", |
|
| 32 |
student_summary_report = "Zoom_Student_Engagement_Analysis_student_summary_report.Rmd", |
|
| 33 |
student_summary_report_folder = system.file("", package = "zoomstudentengagement"),
|
|
| 34 |
output_format = NULL) {
|
|
| 35 |
# DEPRECATED: This function will be removed in the next version |
|
| 36 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 37 | 1x |
warning("Function 'run_student_reports' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 38 | ||
| 39 | 1x |
template <- file.path(student_summary_report_folder, student_summary_report) |
| 40 | 1x |
outputs <- character(0) |
| 41 | 1x |
for (section in df_sections$section) {
|
| 42 | 1x |
target_section <- section |
| 43 |
# Use base R operations to avoid dplyr segfault issues |
|
| 44 | 1x |
section_mask <- df_roster$section == target_section |
| 45 | 1x |
target_students <- df_roster$preferred_name[section_mask] |
| 46 | 1x |
target_students <- c("All Students", target_students)
|
| 47 | 1x |
base_name <- tools::file_path_sans_ext(basename(student_summary_report)) |
| 48 | 1x |
for (target_student in target_students) {
|
| 49 | 2x |
output_file <- file.path( |
| 50 | 2x |
data_folder, |
| 51 | 2x |
sprintf("%s - section %s - %s.html", base_name, target_section, target_student)
|
| 52 |
) |
|
| 53 |
# Add error handling for R Markdown rendering |
|
| 54 | 2x |
tryCatch( |
| 55 |
{
|
|
| 56 | 2x |
rmarkdown::render( |
| 57 | 2x |
template, |
| 58 | 2x |
params = list( |
| 59 | 2x |
target_section = target_section, |
| 60 | 2x |
target_student = target_student, |
| 61 | 2x |
data_folder = data_folder, |
| 62 | 2x |
transcripts_session_summary_file = transcripts_session_summary_file, |
| 63 | 2x |
transcripts_summary_file = transcripts_summary_file |
| 64 |
), |
|
| 65 | 2x |
output_file = output_file, |
| 66 | 2x |
output_format = output_format |
| 67 |
) |
|
| 68 |
}, |
|
| 69 | 2x |
error = function(e) {
|
| 70 | ! |
warning(sprintf( |
| 71 | ! |
"Failed to render report for section %s, student %s: %s", |
| 72 | ! |
target_section, target_student, e$message |
| 73 |
)) |
|
| 74 |
# Create a simple error report instead |
|
| 75 | ! |
error_content <- sprintf( |
| 76 | ! |
"Error generating report for section %s, student %s: %s", |
| 77 | ! |
target_section, target_student, e$message |
| 78 |
) |
|
| 79 | ! |
writeLines(error_content, output_file) |
| 80 |
} |
|
| 81 |
) |
|
| 82 | 2x |
outputs <- c(outputs, output_file) |
| 83 |
} |
|
| 84 |
} |
|
| 85 | 1x |
invisible(outputs) |
| 86 |
} |
| 1 |
#' Internal diagnostics helpers (quiet-by-default) |
|
| 2 |
#' |
|
| 3 |
#' These helpers centralize diagnostic output policy. By default, diagnostics |
|
| 4 |
#' are suppressed. Users can enable verbose diagnostics by setting the option |
|
| 5 |
#' `options(zoomstudentengagement.verbose = TRUE)`. |
|
| 6 |
#' |
|
| 7 |
#' @importFrom utils str |
|
| 8 |
#' |
|
| 9 |
#' @name zse_diagnostics |
|
| 10 |
#' @keywords internal |
|
| 11 |
NULL |
|
| 12 | ||
| 13 |
# Return TRUE if verbose diagnostics are enabled |
|
| 14 |
is_verbose <- function() {
|
|
| 15 |
# DEPRECATED: This function will be removed in the next version |
|
| 16 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 17 | 123x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 18 | 3x |
warning("Function 'is_verbose' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 19 |
} |
|
| 20 | ||
| 21 | 123x |
isTRUE(getOption("zoomstudentengagement.verbose", FALSE))
|
| 22 |
} |
|
| 23 | ||
| 24 |
#' Conditionally emit a message when verbose is enabled |
|
| 25 |
#' |
|
| 26 |
#' @keywords internal |
|
| 27 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 28 |
diag_message <- function(...) {
|
|
| 29 |
# DEPRECATED: This function will be removed in the next version |
|
| 30 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 31 | 78x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 32 | ! |
warning("Function 'diag_message' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 33 |
} |
|
| 34 | ||
| 35 | 78x |
if (is_verbose()) {
|
| 36 | 28x |
message(...) |
| 37 |
} |
|
| 38 | 78x |
invisible(NULL) |
| 39 |
} |
|
| 40 | ||
| 41 |
#' Conditionally emit cat-style output when verbose is enabled or in interactive sessions |
|
| 42 |
#' |
|
| 43 |
#' @keywords internal |
|
| 44 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 45 |
diag_cat <- function(...) {
|
|
| 46 |
# DEPRECATED: This function will be removed in the next version |
|
| 47 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 48 | 6x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 49 | 1x |
warning("Function 'diag_cat' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 50 |
} |
|
| 51 | ||
| 52 | 6x |
if (is_verbose() || interactive()) {
|
| 53 | 3x |
cat(...) |
| 54 |
} |
|
| 55 | 6x |
invisible(NULL) |
| 56 |
} |
|
| 57 | ||
| 58 |
#' Conditionally emit a message if a local verbose flag is TRUE or global verbose is enabled |
|
| 59 |
#' |
|
| 60 |
#' @param verbose_flag Logical flag controlling local verbosity |
|
| 61 |
#' @keywords internal |
|
| 62 |
diag_message_if <- function(verbose_flag, ...) {
|
|
| 63 |
# DEPRECATED: This function will be removed in the next version |
|
| 64 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 65 | 4x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 66 | ! |
warning("Function 'diag_message_if' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 67 |
} |
|
| 68 | ||
| 69 | 4x |
if (isTRUE(verbose_flag) || is_verbose()) {
|
| 70 | 3x |
message(...) |
| 71 |
} |
|
| 72 | 4x |
invisible(NULL) |
| 73 |
} |
|
| 74 | ||
| 75 |
#' Conditionally emit cat output if a local verbose flag is TRUE or global verbose is enabled |
|
| 76 |
#' |
|
| 77 |
#' @param verbose_flag Logical flag controlling local verbosity |
|
| 78 |
#' @keywords internal |
|
| 79 |
diag_cat_if <- function(verbose_flag, ...) {
|
|
| 80 |
# DEPRECATED: This function will be removed in the next version |
|
| 81 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 82 | 3x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 83 | ! |
warning("Function 'diag_cat_if' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 84 |
} |
|
| 85 | ||
| 86 | 3x |
if (isTRUE(verbose_flag) || is_verbose() || interactive()) {
|
| 87 | 2x |
cat(...) |
| 88 |
} |
|
| 89 | 3x |
invisible(NULL) |
| 90 |
} |
| 1 |
#' Add Dead Air Rows |
|
| 2 |
#' |
|
| 3 |
#' Take a tibble containing the comments from a Zoom recording transcript and return a tibble |
|
| 4 |
#' that adds rows for any time between transcribed comments, labeled with the `dead_air_name` |
|
| 5 |
#' provided (or the default value of 'dead_air'). The resulting tibble will have rows |
|
| 6 |
#' accounting for the time from the beginning of the first comment to the end of the last one. |
|
| 7 |
#' |
|
| 8 | ||
| 9 | ||
| 10 | ||
| 11 | ||
| 12 |
#' @param df A tibble containing the comments from a Zoom recording transcript. |
|
| 13 |
#' @param dead_air_name Character string to label the `name` column in the added rows. |
|
| 14 |
#' Defaults to 'dead_air'. |
|
| 15 |
#' |
|
| 16 |
#' @return A tibble containing the comments from a Zoom recording transcript, |
|
| 17 |
#' with rows added for dead air. |
|
| 18 |
#' @export |
|
| 19 |
#' @keywords deprecated |
|
| 20 |
#' |
|
| 21 |
#' @examples |
|
| 22 |
#' add_dead_air_rows(df = "NULL") |
|
| 23 |
#' |
|
| 24 |
# CRAN compliance: global variables handled in package file |
|
| 25 |
add_dead_air_rows <- function(df = NULL, dead_air_name = "dead_air") {
|
|
| 26 |
# DEPRECATED: This function will be removed in the next version |
|
| 27 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 28 | 274x |
warning("Function 'add_dead_air_rows' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 29 | ||
| 30 |
# Removed local NULL assignments; handled by globalVariables above. |
|
| 31 | ||
| 32 | 274x |
if (tibble::is_tibble(df)) {
|
| 33 |
# Ensure time columns are of type hms (replacing lubridate::period to avoid segfaults) |
|
| 34 |
# Use base R operations to avoid dplyr segfaults |
|
| 35 | 270x |
df$start <- hms::as_hms(df$start) |
| 36 | 270x |
df$end <- hms::as_hms(df$end) |
| 37 | ||
| 38 |
# Check if transcript_file column exists (used for conditional logic) |
|
| 39 |
# Note: has_transcript_file variable is currently unused but kept for future use |
|
| 40 | ||
| 41 |
# Create dead air rows using base R to avoid segfaults |
|
| 42 |
# Sort by start time for lag operations |
|
| 43 | 270x |
df <- df[order(df$start), ] |
| 44 | ||
| 45 |
# Calculate lag values using base R |
|
| 46 | 270x |
df$prev_end <- c(hms::hms(0), df$end[-length(df$end)]) |
| 47 | 270x |
df$prior_dead_air <- as.numeric(df$start - df$prev_end) |
| 48 | ||
| 49 |
# Create dead air rows only for gaps (prior_dead_air > 0) |
|
| 50 | 270x |
gap_indices <- which(df$prior_dead_air > 0) |
| 51 | ||
| 52 | 270x |
if (length(gap_indices) > 0) {
|
| 53 | 216x |
dead_air_rows <- df[gap_indices, , drop = FALSE] |
| 54 | 216x |
dead_air_rows$name <- dead_air_name |
| 55 | 216x |
dead_air_rows$comment <- NA |
| 56 | 216x |
dead_air_rows$duration <- dead_air_rows$prior_dead_air |
| 57 | 216x |
dead_air_rows$end <- dead_air_rows$start |
| 58 | 216x |
dead_air_rows$start <- dead_air_rows$prev_end |
| 59 | ||
| 60 |
# Only add columns that exist in the original dataframe |
|
| 61 | 216x |
if ("raw_end" %in% names(df)) {
|
| 62 | 1x |
dead_air_rows$raw_end <- NA |
| 63 |
} |
|
| 64 | 216x |
if ("raw_start" %in% names(df)) {
|
| 65 | 1x |
dead_air_rows$raw_start <- NA |
| 66 |
} |
|
| 67 | 216x |
if ("wordcount" %in% names(df)) {
|
| 68 | 202x |
dead_air_rows$wordcount <- NA |
| 69 |
} |
|
| 70 | ||
| 71 |
# Remove temporary columns from both dataframes to ensure matching structure |
|
| 72 | 216x |
dead_air_rows$prior_dead_air <- NULL |
| 73 | 216x |
dead_air_rows$prev_end <- NULL |
| 74 | ||
| 75 |
# Also remove these columns from the original df if they exist |
|
| 76 | 216x |
if ("prior_dead_air" %in% names(df)) {
|
| 77 | 216x |
df$prior_dead_air <- NULL |
| 78 |
} |
|
| 79 | 216x |
if ("prev_end" %in% names(df)) {
|
| 80 | 216x |
df$prev_end <- NULL |
| 81 |
} |
|
| 82 | ||
| 83 |
# Ensure both dataframes have the same column order |
|
| 84 | 216x |
common_cols <- intersect(names(df), names(dead_air_rows)) |
| 85 | 216x |
df <- df[, common_cols, drop = FALSE] |
| 86 | 216x |
dead_air_rows <- dead_air_rows[, common_cols, drop = FALSE] |
| 87 | ||
| 88 |
# Combine original and dead air rows using base R |
|
| 89 | 216x |
result <- rbind(df, dead_air_rows) |
| 90 |
} else {
|
|
| 91 |
# No gaps, just return original data |
|
| 92 | 54x |
if ("prior_dead_air" %in% names(df)) {
|
| 93 | 54x |
df$prior_dead_air <- NULL |
| 94 |
} |
|
| 95 | 54x |
if ("prev_end" %in% names(df)) {
|
| 96 | 54x |
df$prev_end <- NULL |
| 97 |
} |
|
| 98 | 54x |
result <- df |
| 99 |
} |
|
| 100 | 270x |
return(tibble::as_tibble(result)) |
| 101 |
} |
|
| 102 |
} |
| 1 |
#' Write Section Names Lookup |
|
| 2 |
#' |
|
| 3 |
#' This function takes a tibble containing session details and summary metrics |
|
| 4 |
#' by speaker for all class sessions (and placeholders for missing sections), |
|
| 5 |
#' including customized student names (`clean_names_df`) and saves a subset as a |
|
| 6 |
#' csv file with the specified file name (`section_names_lookup_file`) to the |
|
| 7 |
#' specified data folder (`data_folder`). |
|
| 8 |
#' |
|
| 9 |
#' @param clean_names_df A tibble containing session details and summary metrics |
|
| 10 |
#' by speaker for all class sessions (and placeholders for missing sections), |
|
| 11 |
#' including customized student names. |
|
| 12 |
#' @param data_folder Overall data folder for your recordings and data. Defaults |
|
| 13 |
#' to 'data' |
|
| 14 |
#' @param section_names_lookup_file File name of the csv file of customized |
|
| 15 |
#' student names by section Defaults to 'section_names_lookup.csv' |
|
| 16 |
#' |
|
| 17 |
#' @return A tibble corresponding to the csv file saved, which is a sorted subset |
|
| 18 |
#' of the provided tibble containing session details and summary metrics by |
|
| 19 |
#' speaker for all class sessions (and placeholders for missing sections), |
|
| 20 |
#' including customized student names. |
|
| 21 |
#' @export |
|
| 22 |
#' |
|
| 23 |
#' @examples |
|
| 24 |
#' # Create sample data |
|
| 25 |
#' sample_transcript_list <- tibble::tibble( |
|
| 26 |
#' name = c("Student A", "Student B", "Student C"),
|
|
| 27 |
#' course_section = c("101.A", "101.A", "101.B"),
|
|
| 28 |
#' course = c(101, 101, 101), |
|
| 29 |
#' section = c("A", "A", "B"),
|
|
| 30 |
#' day = c("Monday", "Monday", "Tuesday"),
|
|
| 31 |
#' time = c("9:00 AM", "9:00 AM", "10:00 AM"),
|
|
| 32 |
#' n = c(10, 8, 12), |
|
| 33 |
#' duration = c(300, 240, 360), |
|
| 34 |
#' wordcount = c(500, 400, 600), |
|
| 35 |
#' comments = c("Good", "Excellent", "Average"),
|
|
| 36 |
#' n_perc = c(0.4, 0.3, 0.5), |
|
| 37 |
#' duration_perc = c(0.4, 0.3, 0.5), |
|
| 38 |
#' wordcount_perc = c(0.4, 0.3, 0.5), |
|
| 39 |
#' wpm = c(100, 100, 100), |
|
| 40 |
#' name_raw = c("Student A", "Student B", "Student C"),
|
|
| 41 |
#' start_time_local = c("2024-01-01 09:00:00", "2024-01-01 09:00:00", "2024-01-02 10:00:00"),
|
|
| 42 |
#' dept = c("CS", "CS", "CS"),
|
|
| 43 |
#' session_num = c(1, 1, 2) |
|
| 44 |
#' ) |
|
| 45 |
#' |
|
| 46 |
#' sample_roster <- tibble::tibble( |
|
| 47 |
#' first_last = c("Student A", "Student B", "Student C"),
|
|
| 48 |
#' preferred_name = c("Student A", "Student B", "Student C"),
|
|
| 49 |
#' course = c("101", "101", "101"),
|
|
| 50 |
#' section = c("A", "A", "B"),
|
|
| 51 |
#' student_id = c("A123", "B456", "C789"),
|
|
| 52 |
#' dept = c("CS", "CS", "CS"),
|
|
| 53 |
#' session_num = c(1, 1, 2), |
|
| 54 |
#' start_time_local = c("2024-01-01 09:00:00", "2024-01-01 09:00:00", "2024-01-02 10:00:00"),
|
|
| 55 |
#' course_section = c("101.A", "101.A", "101.B")
|
|
| 56 |
#' ) |
|
| 57 |
#' |
|
| 58 |
#' # Create a temporary directory for the example |
|
| 59 |
#' temp_dir <- tempfile("example")
|
|
| 60 |
#' dir.create(temp_dir) |
|
| 61 |
#' |
|
| 62 |
#' # Run the example with the temporary directory |
|
| 63 |
#' write_section_names_lookup( |
|
| 64 |
#' clean_names_df = make_clean_names_df( |
|
| 65 |
#' data_folder = temp_dir, |
|
| 66 |
#' section_names_lookup_file = "section_names_lookup.csv", |
|
| 67 |
#' transcripts_metrics_df = sample_transcript_list, |
|
| 68 |
#' roster_sessions = sample_roster |
|
| 69 |
#' ), |
|
| 70 |
#' data_folder = temp_dir, |
|
| 71 |
#' section_names_lookup_file = "section_names_lookup.csv" |
|
| 72 |
#' ) |
|
| 73 |
#' |
|
| 74 |
#' # Clean up |
|
| 75 |
#' unlink(temp_dir, recursive = TRUE) |
|
| 76 |
write_section_names_lookup <- |
|
| 77 |
function(clean_names_df = NULL, |
|
| 78 |
data_folder = ".", |
|
| 79 |
section_names_lookup_file = "section_names_lookup.csv") {
|
|
| 80 | 4x |
course <- |
| 81 | 4x |
day <- |
| 82 | 4x |
formal_name <- |
| 83 | 4x |
n <- |
| 84 | 4x |
preferred_name <- |
| 85 | 4x |
section <- |
| 86 | 4x |
student_id <- time <- transcript_name <- course_section <- NULL |
| 87 | ||
| 88 | 4x |
if (tibble::is_tibble(clean_names_df) && |
| 89 | 4x |
file.exists(data_folder) |
| 90 |
) {
|
|
| 91 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 92 |
# Group by the specified columns and count occurrences |
|
| 93 | 3x |
group_cols <- c( |
| 94 | 3x |
"course_section", "day", "time", "course", "section", |
| 95 | 3x |
"preferred_name", "formal_name", "transcript_name", "student_id" |
| 96 |
) |
|
| 97 | ||
| 98 |
# Create a unique identifier for each group |
|
| 99 | 3x |
clean_names_df$group_id <- apply(clean_names_df[, group_cols], 1, paste, collapse = "|") |
| 100 | ||
| 101 |
# Get the first row from each group (equivalent to summarise) |
|
| 102 | 3x |
result <- clean_names_df[!duplicated(clean_names_df$group_id), group_cols, drop = FALSE] |
| 103 | ||
| 104 |
# Sort by preferred_name and formal_name using base R |
|
| 105 | 3x |
result <- result[order(result$preferred_name, result$formal_name), , drop = FALSE] |
| 106 | ||
| 107 |
# Write to CSV |
|
| 108 | 3x |
readr::write_csv(result, file.path(data_folder, section_names_lookup_file)) |
| 109 | ||
| 110 |
# Return the result tibble |
|
| 111 | 3x |
return(tibble::as_tibble(result)) |
| 112 |
} |
|
| 113 |
} |
| 1 |
#' Mask User Names by Metric |
|
| 2 |
#' |
|
| 3 |
#' @param df a tibble that summarizes results at the level of the class section |
|
| 4 |
#' and student. This tibble will have the student names replaced by the |
|
| 5 |
#' ranking of the student. If a `target_student` preferred name is provided, |
|
| 6 |
#' that student's name will be bolded using markdown syntax and not masked. |
|
| 7 |
#' @param metric Label of the metric to use to order the students. Defaults to |
|
| 8 |
#' 'session_ct'. |
|
| 9 |
#' @param target_student preferred student name of an individual student that |
|
| 10 |
#' will be bolded using markdown syntax and not masked. Defaults to ''. |
|
| 11 |
#' |
|
| 12 |
#' @return a tibble that summarizes results at the level of the class section |
|
| 13 |
#' and student, with student names masked by the ranking of the student. |
|
| 14 |
#' @export |
|
| 15 |
#' |
|
| 16 |
#' @examples |
|
| 17 |
#' # Create sample transcripts summary data |
|
| 18 |
#' sample_summary <- tibble::tibble( |
|
| 19 |
#' section = c("101.A", "101.A", "101.A"),
|
|
| 20 |
#' preferred_name = c("John Smith", "Jane Doe", "Bob Wilson"),
|
|
| 21 |
#' session_ct = c(5, 3, 8), |
|
| 22 |
#' duration = c(300, 180, 480), |
|
| 23 |
#' wordcount = c(500, 300, 800) |
|
| 24 |
#' ) |
|
| 25 |
#' |
|
| 26 |
#' # Mask student names by session count (default metric) |
|
| 27 |
#' mask_user_names_by_metric(sample_summary) |
|
| 28 |
#' |
|
| 29 |
#' # Mask student names by duration metric |
|
| 30 |
#' mask_user_names_by_metric(sample_summary, metric = "duration") |
|
| 31 |
#' |
|
| 32 |
#' # Highlight a specific student while masking others |
|
| 33 |
#' mask_user_names_by_metric(sample_summary, target_student = "Jane Doe") |
|
| 34 |
#' |
|
| 35 |
#' \dontrun{
|
|
| 36 |
#' # More complex example with larger dataset |
|
| 37 |
#' # Create sample transcripts summary data |
|
| 38 |
#' sample_summary <- tibble::tibble( |
|
| 39 |
#' section = c("101.A", "101.A", "101.A"),
|
|
| 40 |
#' preferred_name = c("John Smith", "Jane Doe", "Bob Wilson"),
|
|
| 41 |
#' session_ct = c(5, 3, 8), |
|
| 42 |
#' duration = c(300, 180, 480), |
|
| 43 |
#' wordcount = c(500, 300, 800) |
|
| 44 |
#' ) |
|
| 45 |
#' |
|
| 46 |
#' # Mask student names by session count (default metric) |
|
| 47 |
#' mask_user_names_by_metric(sample_summary) |
|
| 48 |
#' |
|
| 49 |
#' # Mask student names by duration metric |
|
| 50 |
#' mask_user_names_by_metric(sample_summary, metric = "duration") |
|
| 51 |
#' |
|
| 52 |
#' # Highlight a specific student while masking others |
|
| 53 |
#' mask_user_names_by_metric(sample_summary, target_student = "Jane Doe") |
|
| 54 |
#' } |
|
| 55 |
mask_user_names_by_metric <- |
|
| 56 |
function(df = NULL, |
|
| 57 |
metric = "session_ct", |
|
| 58 |
target_student = "") {
|
|
| 59 | 38x |
row_num <- preferred_name <- section <- NULL |
| 60 | ||
| 61 | 38x |
if (tibble::is_tibble(df)) {
|
| 62 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 63 | 38x |
if (!metric %in% names(df)) {
|
| 64 | 1x |
stop(sprintf("Metric '%s' not found in data", metric), call. = FALSE)
|
| 65 |
} |
|
| 66 | 37x |
metric_col <- df[[metric]] |
| 67 | ||
| 68 |
# Handle NA values by replacing with -Inf for sorting |
|
| 69 | 37x |
metric_col_clean <- ifelse(is.na(metric_col), -Inf, metric_col) |
| 70 | ||
| 71 |
# Sort by metric (descending) and get row numbers |
|
| 72 | 37x |
sorted_indices <- order(metric_col_clean, decreasing = TRUE) |
| 73 | 37x |
row_numbers <- match(seq_along(metric_col_clean), sorted_indices) |
| 74 | ||
| 75 |
# Create student names using base R |
|
| 76 | 37x |
student_names <- character(length(row_numbers)) |
| 77 | 37x |
for (i in seq_along(row_numbers)) {
|
| 78 | 228x |
name_i <- df$preferred_name[i] |
| 79 | 228x |
if (!is.na(name_i) && nzchar(target_student) && identical(name_i, target_student)) {
|
| 80 | 1x |
student_names[i] <- paste0("**", target_student, "**")
|
| 81 |
} else {
|
|
| 82 | 227x |
student_names[i] <- paste("Student", stringr::str_pad(row_numbers[i], width = 2, pad = "0"), sep = " ")
|
| 83 |
} |
|
| 84 |
} |
|
| 85 | ||
| 86 |
# Create result dataframe |
|
| 87 | 37x |
result <- df |
| 88 | 37x |
result$student <- student_names |
| 89 | ||
| 90 |
# Convert to tibble to maintain expected return type |
|
| 91 | 37x |
return(tibble::as_tibble(result)) |
| 92 |
} |
|
| 93 |
} |
| 1 |
#' Create Analysis Configuration |
|
| 2 |
#' |
|
| 3 |
#' This function creates a validated configuration object for the zoomstudentengagement |
|
| 4 |
#' package analysis workflow. It groups related parameters logically and provides |
|
| 5 |
#' sensible defaults while allowing customization for different course setups. |
|
| 6 |
#' |
|
| 7 |
#' @param dept Department code (e.g., "LTF", "MATH", "CS"). Used to filter Zoom recordings. |
|
| 8 |
#' @param semester_start_mdy Semester start date in "MMM DD, YYYY" format (e.g., "Jan 01, 2024"). |
|
| 9 |
#' @param scheduled_session_length_hours Scheduled length of each class session in hours. |
|
| 10 |
#' @param instructor_name Name of the instructor for filtering and reporting. |
|
| 11 |
#' @param data_folder Overall data folder for recordings and data files. |
|
| 12 |
#' @param transcripts_folder Subfolder within data_folder where transcript files are stored. |
|
| 13 |
#' @param roster_file Name of the CSV file containing student roster information. |
|
| 14 |
#' @param cancelled_classes_file Name of the CSV file containing cancelled class information. |
|
| 15 |
#' @param names_lookup_file Name of the CSV file containing section names lookup information. |
|
| 16 |
#' @param transcripts_session_summary_file Name of the output CSV file for session-level summaries. |
|
| 17 |
#' @param transcripts_summary_file Name of the output CSV file for overall summaries. |
|
| 18 |
#' @param student_summary_report Base name for student summary report files. |
|
| 19 |
#' @param student_summary_report_folder Folder where student summary report templates are stored. |
|
| 20 |
#' @param topic_split_pattern Regex pattern to parse Zoom recording topics and extract course information. |
|
| 21 |
#' @param zoom_recorded_sessions_csv_names_pattern Regex pattern to match Zoom cloud recording CSV files. |
|
| 22 |
#' @param zoom_recorded_sessions_csv_col_names Comma-separated column names for Zoom cloud recording CSVs. |
|
| 23 |
#' @param transcript_files_names_pattern Regex pattern to match transcript file names. |
|
| 24 |
#' @param dt_extract_pattern Regex pattern to extract date from transcript file names. |
|
| 25 |
#' @param transcript_file_extension_pattern Regex pattern to identify transcript files. |
|
| 26 |
#' @param closed_caption_file_extension_pattern Regex pattern to identify closed caption files. |
|
| 27 |
#' @param recording_start_pattern Regex pattern to extract recording start time from file names. |
|
| 28 |
#' @param recording_start_format Format string for parsing recording start times. |
|
| 29 |
#' @param start_time_local_tzone Local timezone for recording start times. |
|
| 30 |
#' @param cancelled_classes_col_types Column types specification for cancelled classes CSV. |
|
| 31 |
#' @param section_names_lookup_col_types Column types specification for section names lookup CSV. |
|
| 32 |
#' @param names_to_exclude Character vector of names to exclude from analysis (e.g., "dead_air"). |
|
| 33 |
#' @param use_session_mapping If TRUE, use session mapping approach instead of regex parsing |
|
| 34 |
#' @param session_mapping_file Path to session mapping CSV file (if use_session_mapping = TRUE) |
|
| 35 |
#' |
|
| 36 |
#' @return A list containing the configuration organized into logical groups: |
|
| 37 |
#' - course: Course-specific information |
|
| 38 |
#' - paths: File and folder paths |
|
| 39 |
#' - patterns: Regex patterns for file matching and parsing |
|
| 40 |
#' - reports: Report generation settings |
|
| 41 |
#' - analysis: Analysis-specific parameters |
|
| 42 |
#' |
|
| 43 |
#' @export |
|
| 44 |
#' |
|
| 45 |
#' @examples |
|
| 46 |
#' # Basic configuration with defaults |
|
| 47 |
#' config <- create_analysis_config( |
|
| 48 |
#' dept = "LTF", |
|
| 49 |
#' instructor_name = "Dr. Smith", |
|
| 50 |
#' data_folder = "data" |
|
| 51 |
#' ) |
|
| 52 |
#' |
|
| 53 |
#' # Custom configuration for different course setup |
|
| 54 |
#' config <- create_analysis_config( |
|
| 55 |
#' dept = "MATH", |
|
| 56 |
#' semester_start_mdy = "Aug 28, 2024", |
|
| 57 |
#' scheduled_session_length_hours = 2.0, |
|
| 58 |
#' instructor_name = "Prof. Johnson", |
|
| 59 |
#' data_folder = "math_101_data", |
|
| 60 |
#' transcripts_folder = "zoom_recordings", |
|
| 61 |
#' start_time_local_tzone = "America/New_York" |
|
| 62 |
#' ) |
|
| 63 |
#' |
|
| 64 |
#' # Use configuration in analysis workflow |
|
| 65 |
#' zoom_recorded_sessions_df <- load_zoom_recorded_sessions_list( |
|
| 66 |
#' data_folder = config$paths$data_folder, |
|
| 67 |
#' transcripts_folder = config$paths$transcripts_folder, |
|
| 68 |
#' topic_split_pattern = config$patterns$topic_split, |
|
| 69 |
#' zoom_recorded_sessions_csv_names_pattern = config$patterns$zoom_recordings_csv, |
|
| 70 |
#' dept = config$course$dept, |
|
| 71 |
#' semester_start_mdy = config$course$semester_start, |
|
| 72 |
#' scheduled_session_length_hours = config$course$session_length_hours |
|
| 73 |
#' ) |
|
| 74 |
create_analysis_config <- function( |
|
| 75 |
# Course Information |
|
| 76 |
dept = "LTF", |
|
| 77 |
semester_start_mdy = "Jan 01, 2024", |
|
| 78 |
scheduled_session_length_hours = 1.5, |
|
| 79 |
instructor_name = "Conor Healy", |
|
| 80 |
# File Paths |
|
| 81 |
data_folder = system.file("extdata", package = "zoomstudentengagement"),
|
|
| 82 |
transcripts_folder = "transcripts", |
|
| 83 |
roster_file = "roster.csv", |
|
| 84 |
cancelled_classes_file = "cancelled_classes.csv", |
|
| 85 |
names_lookup_file = "section_names_lookup.csv", |
|
| 86 |
transcripts_session_summary_file = "transcripts_session_summary.csv", |
|
| 87 |
transcripts_summary_file = "transcripts_summary.csv", |
|
| 88 |
# Report Settings |
|
| 89 |
student_summary_report = "Zoom_Student_Engagement_Analysis_student_summary_report", |
|
| 90 |
student_summary_report_folder = system.file("", package = "zoomstudentengagement"),
|
|
| 91 |
# File Patterns |
|
| 92 |
topic_split_pattern = paste0( |
|
| 93 |
"^(?<dept>\\S+) (?<section>\\S+) - ", |
|
| 94 |
"(?<day>[A-Za-z]+) (?<time>\\S+\\s*\\S+) (?<instructor>\\(.*?\\))" |
|
| 95 |
), |
|
| 96 |
zoom_recorded_sessions_csv_names_pattern = "zoomus_recordings__\\d{8}(?:\\s+copy\\s*\\d*)?\\.csv",
|
|
| 97 |
zoom_recorded_sessions_csv_col_names = paste0( |
|
| 98 |
"Topic,ID,Start Time,File Size (MB),File Count,", |
|
| 99 |
"Total Views,Total Downloads,Last Accessed" |
|
| 100 |
), |
|
| 101 |
# Transcript File Patterns |
|
| 102 |
transcript_files_names_pattern = "GMT\\d{8}-\\d{6}_Recording",
|
|
| 103 |
dt_extract_pattern = "(?<=GMT)\\d{8}",
|
|
| 104 |
transcript_file_extension_pattern = ".transcript", |
|
| 105 |
closed_caption_file_extension_pattern = ".cc", |
|
| 106 |
recording_start_pattern = "(?<=GMT)\\d{8}-\\d{6}",
|
|
| 107 |
recording_start_format = "%Y%m%d-%H%M%S", |
|
| 108 |
start_time_local_tzone = "America/Los_Angeles", |
|
| 109 |
# Column Types |
|
| 110 |
cancelled_classes_col_types = "ciiiccccccdiiicTTcTTccci", |
|
| 111 |
section_names_lookup_col_types = "ccccccccc", |
|
| 112 |
# Analysis Parameters |
|
| 113 |
names_to_exclude = NULL, |
|
| 114 |
# Session Mapping |
|
| 115 |
use_session_mapping = FALSE, |
|
| 116 |
session_mapping_file = "session_mapping.csv") {
|
|
| 117 |
# DEPRECATED: This function will be removed in the next version |
|
| 118 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 119 | 17x |
warning("Function 'create_analysis_config' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 120 | ||
| 121 |
# Input validation |
|
| 122 | 17x |
if (!is.character(dept) || length(dept) != 1) {
|
| 123 | 2x |
stop("dept must be a single character string")
|
| 124 |
} |
|
| 125 | 15x |
if (!is.character(semester_start_mdy) || length(semester_start_mdy) != 1) {
|
| 126 | 1x |
stop("semester_start_mdy must be a single character string")
|
| 127 |
} |
|
| 128 | 14x |
if (!is.numeric(scheduled_session_length_hours) || scheduled_session_length_hours <= 0) {
|
| 129 | 3x |
stop("scheduled_session_length_hours must be a positive number")
|
| 130 |
} |
|
| 131 | 11x |
if (!is.character(instructor_name) || length(instructor_name) != 1) {
|
| 132 | 1x |
stop("instructor_name must be a single character string")
|
| 133 |
} |
|
| 134 | 10x |
if (!is.character(data_folder) || length(data_folder) != 1) {
|
| 135 | 1x |
stop("data_folder must be a single character string")
|
| 136 |
} |
|
| 137 | 9x |
if (!is.character(transcripts_folder) || length(transcripts_folder) != 1) {
|
| 138 | 1x |
stop("transcripts_folder must be a single character string")
|
| 139 |
} |
|
| 140 | 8x |
if (!is.character(start_time_local_tzone) || length(start_time_local_tzone) != 1) {
|
| 141 | 1x |
stop("start_time_local_tzone must be a single character string")
|
| 142 |
} |
|
| 143 | ||
| 144 |
# Return validated configuration |
|
| 145 | 7x |
list( |
| 146 | 7x |
course = list( |
| 147 | 7x |
dept = dept, |
| 148 | 7x |
semester_start = semester_start_mdy, |
| 149 | 7x |
session_length_hours = scheduled_session_length_hours, |
| 150 | 7x |
instructor_name = instructor_name |
| 151 |
), |
|
| 152 | 7x |
paths = list( |
| 153 | 7x |
data_folder = data_folder, |
| 154 | 7x |
transcripts_folder = transcripts_folder, |
| 155 | 7x |
roster_file = roster_file, |
| 156 | 7x |
cancelled_classes_file = cancelled_classes_file, |
| 157 | 7x |
names_lookup_file = names_lookup_file, |
| 158 | 7x |
transcripts_session_summary_file = transcripts_session_summary_file, |
| 159 | 7x |
transcripts_summary_file = transcripts_summary_file |
| 160 |
), |
|
| 161 | 7x |
patterns = list( |
| 162 | 7x |
topic_split = topic_split_pattern, |
| 163 | 7x |
zoom_recordings_csv = zoom_recorded_sessions_csv_names_pattern, |
| 164 | 7x |
zoom_recordings_csv_col_names = zoom_recorded_sessions_csv_col_names, |
| 165 | 7x |
transcript_files_names = transcript_files_names_pattern, |
| 166 | 7x |
dt_extract = dt_extract_pattern, |
| 167 | 7x |
transcript_file_extension = transcript_file_extension_pattern, |
| 168 | 7x |
closed_caption_file_extension = closed_caption_file_extension_pattern, |
| 169 | 7x |
recording_start = recording_start_pattern, |
| 170 | 7x |
recording_start_format = recording_start_format, |
| 171 | 7x |
start_time_local_tzone = start_time_local_tzone |
| 172 |
), |
|
| 173 | 7x |
reports = list( |
| 174 | 7x |
student_summary_report = student_summary_report, |
| 175 | 7x |
student_summary_report_folder = student_summary_report_folder |
| 176 |
), |
|
| 177 | 7x |
analysis = list( |
| 178 | 7x |
cancelled_classes_col_types = cancelled_classes_col_types, |
| 179 | 7x |
section_names_lookup_col_types = section_names_lookup_col_types, |
| 180 | 7x |
names_to_exclude = names_to_exclude |
| 181 |
), |
|
| 182 | 7x |
session_mapping = list( |
| 183 | 7x |
use_session_mapping = use_session_mapping, |
| 184 | 7x |
session_mapping_file = session_mapping_file |
| 185 |
) |
|
| 186 |
) |
|
| 187 |
} |
| 1 |
#' Make Students Only Transcripts Summary |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from summary results at the |
|
| 4 |
#' level of the class section and preferred student name after filtering for only the students enrolled in the class. |
|
| 5 |
#' |
|
| 6 |
#' @param transcripts_session_summary_df A tibble that summarizes results at the level of the class section and preferred student name. |
|
| 7 |
#' @param preferred_name_exclude_cv A character vector of names to exclude from the results. Defaults to c("dead_air", "Instructor Name", "Guests", "unknown").
|
|
| 8 |
#' |
|
| 9 |
#' @return A tibble that summarizes results at the level of the class section and preferred student name for only the students enrolled in the class. |
|
| 10 |
#' @export |
|
| 11 |
#' |
|
| 12 |
#' @examples |
|
| 13 |
#' \dontrun{
|
|
| 14 |
#' # Create sample transcript list |
|
| 15 |
#' sample_transcript_list <- tibble::tibble( |
|
| 16 |
#' name = c("John Smith", "Jane Doe", "Unknown"),
|
|
| 17 |
#' course_section = c("101.A", "101.A", "101.A"),
|
|
| 18 |
#' course = c(101, 101, 101), |
|
| 19 |
#' section = c("A", "A", "A"),
|
|
| 20 |
#' day = c("2024-01-01", "2024-01-01", "2024-01-01"),
|
|
| 21 |
#' time = c("10:00", "10:00", "10:00"),
|
|
| 22 |
#' n = c(5, 3, 1), |
|
| 23 |
#' duration = c(300, 180, 60), |
|
| 24 |
#' wordcount = c(500, 300, 100), |
|
| 25 |
#' comments = c(10, 5, 2), |
|
| 26 |
#' n_perc = c(0.5, 0.3, 0.1), |
|
| 27 |
#' duration_perc = c(0.5, 0.3, 0.1), |
|
| 28 |
#' wordcount_perc = c(0.5, 0.3, 0.1), |
|
| 29 |
#' wpm = c(100, 100, 100), |
|
| 30 |
#' name_raw = c("John Smith", "Jane Doe", "Unknown"),
|
|
| 31 |
#' start_time_local = c("2024-01-01 10:00:00", "2024-01-01 10:00:00", "2024-01-01 10:00:00"),
|
|
| 32 |
#' dept = c("CS", "CS", "CS"),
|
|
| 33 |
#' session_num = c(1, 1, 1) |
|
| 34 |
#' ) |
|
| 35 |
#' |
|
| 36 |
#' # Create sample roster |
|
| 37 |
#' sample_roster <- tibble::tibble( |
|
| 38 |
#' first_last = c("John Smith", "Jane Doe"),
|
|
| 39 |
#' preferred_name = c("John Smith", "Jane Doe"),
|
|
| 40 |
#' course = c("101", "101"),
|
|
| 41 |
#' section = c("A", "A"),
|
|
| 42 |
#' student_id = c("12345", "67890"),
|
|
| 43 |
#' dept = c("CS", "CS"),
|
|
| 44 |
#' session_num = c(1, 1), |
|
| 45 |
#' start_time_local = c("2024-01-01 10:00:00", "2024-01-01 10:00:00"),
|
|
| 46 |
#' course_section = c("101.A", "101.A")
|
|
| 47 |
#' ) |
|
| 48 |
#' |
|
| 49 |
#' make_students_only_transcripts_summary_df( |
|
| 50 |
#' make_transcripts_session_summary_df( |
|
| 51 |
#' clean_names_df = make_clean_names_df( |
|
| 52 |
#' data_folder = "data", |
|
| 53 |
#' section_names_lookup_file = "section_names_lookup.csv", |
|
| 54 |
#' transcripts_metrics_df = sample_transcript_list, |
|
| 55 |
#' roster_sessions = sample_roster |
|
| 56 |
#' ) |
|
| 57 |
#' ), |
|
| 58 |
#' preferred_name_exclude_cv = c("dead_air", "Instructor Name", "Guests", "unknown")
|
|
| 59 |
#' ) |
|
| 60 |
#' } |
|
| 61 |
make_students_only_transcripts_summary_df <- |
|
| 62 |
function(transcripts_session_summary_df = NULL, |
|
| 63 |
preferred_name_exclude_cv = c("dead_air", "Instructor Name", "Guests", "unknown")) {
|
|
| 64 | 5x |
section <- NULL |
| 65 | ||
| 66 | 5x |
if (tibble::is_tibble(transcripts_session_summary_df) |
| 67 |
) {
|
|
| 68 |
# Use base R operations instead of dplyr to avoid segmentation fault |
|
| 69 |
# Filter for valid sections and student names |
|
| 70 | 3x |
valid_sections <- !is.na(transcripts_session_summary_df$section) |
| 71 | 3x |
valid_names <- !is.na(transcripts_session_summary_df$preferred_name) |
| 72 | 3x |
not_excluded <- !(transcripts_session_summary_df$preferred_name %in% preferred_name_exclude_cv) |
| 73 | ||
| 74 |
# Combine all filter conditions |
|
| 75 | 3x |
keep_rows <- valid_sections & valid_names & not_excluded |
| 76 | ||
| 77 |
# Apply filter |
|
| 78 | 3x |
filtered_df <- transcripts_session_summary_df[keep_rows, , drop = FALSE] |
| 79 | ||
| 80 |
# Call make_transcripts_summary_df on filtered data |
|
| 81 | 3x |
make_transcripts_summary_df(filtered_df) |
| 82 |
} |
|
| 83 |
} |
|
| 84 | ||
| 85 | ||
| 86 |
# |
|
| 87 |
# make_students_only_transcripts_summary_df <- |
|
| 88 |
# function(df, |
|
| 89 |
# preferred_name_exclude_cv = c( |
|
| 90 |
# "dead_air", |
|
| 91 |
# "Instructor Name", |
|
| 92 |
# "Guests", |
|
| 93 |
# 'unknown') |
|
| 94 |
# ) {
|
|
| 95 |
# |
|
| 96 |
# df %>% |
|
| 97 |
# filter(!preferred_name %in% preferred_name_exclude_cv, |
|
| 98 |
# !is.na(preferred_name), |
|
| 99 |
# !is.na(student_id) |
|
| 100 |
# ) %>% |
|
| 101 |
# group_by(section, day, time, session_num, preferred_name, course_section) %>% |
|
| 102 |
# summarise( |
|
| 103 |
# n = sum(n), |
|
| 104 |
# duration = sum(duration), |
|
| 105 |
# wordcount = sum(wordcount) |
|
| 106 |
# ) %>% |
|
| 107 |
# ungroup() %>% |
|
| 108 |
# group_by(section, preferred_name) %>% |
|
| 109 |
# summarise( |
|
| 110 |
# session_ct = sum(!is.na(duration)), |
|
| 111 |
# n = sum(n), |
|
| 112 |
# duration = sum(duration), |
|
| 113 |
# wordcount = sum(wordcount), |
|
| 114 |
# across(starts_with('session_'), ~ max(.x, na.rm = F))
|
|
| 115 |
# ) %>% |
|
| 116 |
# ungroup() %>% |
|
| 117 |
# group_by(section) %>% |
|
| 118 |
# mutate( |
|
| 119 |
# wpm = wordcount / duration, |
|
| 120 |
# perc_n = n / sum(n, na.rm = TRUE) * 100, |
|
| 121 |
# perc_duration = duration / sum(duration, na.rm = TRUE) * 100, |
|
| 122 |
# perc_wordcount = wordcount / sum(wordcount, na.rm = TRUE) * 100 |
|
| 123 |
# ) %>% |
|
| 124 |
# ungroup() %>% |
|
| 125 |
# arrange(-duration) |
|
| 126 |
# } |
| 1 |
#' Make Semester DF |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble for the units in the semester that correspond |
|
| 4 |
#' to expected number of units, with class start time, end time, and duration. |
|
| 5 |
#' @param semester_units Number of units/weeks/classes in the semester. Defaults |
|
| 6 |
#' to 14. |
|
| 7 |
#' @param class_start_time_gmt Start time of the class in GMT, formatted as |
|
| 8 |
#' 'HH:MM:SS'. Defaults to '04:00:00'. |
|
| 9 |
#' @param class_duration_min Number of units/weeks/classes in the semester. |
|
| 10 |
#' Defaults to 14. |
|
| 11 |
#' @keywords semester |
|
| 12 |
#' @return A tibble including number of units, with class start time, end time, |
|
| 13 |
#' and duration. |
|
| 14 | ||
| 15 |
#' @export |
|
| 16 |
#' @examples |
|
| 17 |
#' make_semester_df() |
|
| 18 |
make_semester_df <- function(semester_units = 14, |
|
| 19 |
class_start_time_gmt = "04:00:00", |
|
| 20 |
class_duration_min = 90) {
|
|
| 21 |
# DEPRECATED: This function will be removed in the next version |
|
| 22 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 23 | 4x |
warning("Function 'make_semester_df' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 24 | ||
| 25 | 4x |
start_time_gmt <- end_time_gmt <- NULL |
| 26 | ||
| 27 | 4x |
if (semester_units <= 0) {
|
| 28 | 1x |
return(tibble::tibble( |
| 29 | 1x |
unit = integer(), |
| 30 | 1x |
start_time_gmt = hms::as_hms(character()), |
| 31 | 1x |
end_time_gmt = hms::as_hms(character()), |
| 32 | 1x |
duration = as.difftime(numeric(), units = "secs"), |
| 33 | 1x |
d2 = as.difftime(numeric(), units = "mins") |
| 34 |
)) |
|
| 35 |
} |
|
| 36 | ||
| 37 | 3x |
class_start_time_gmt <- hms::as_hms(class_start_time_gmt) |
| 38 | 3x |
class_end_time_gmt <- hms::as_hms(class_start_time_gmt + (class_duration_min * 60)) |
| 39 | ||
| 40 | 3x |
tibble::tibble( |
| 41 | 3x |
unit = 1:semester_units, |
| 42 | 3x |
start_time_gmt = class_start_time_gmt, |
| 43 | 3x |
end_time_gmt = class_end_time_gmt, |
| 44 | 3x |
duration = end_time_gmt - start_time_gmt, |
| 45 | 3x |
d2 = base::difftime(end_time_gmt, |
| 46 | 3x |
start_time_gmt, |
| 47 | 3x |
units = c("mins")
|
| 48 |
) |
|
| 49 |
) |
|
| 50 |
} |
| 1 |
#' Make a Smaller DF of the Student Roster |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble that includes rows for each students enrolled |
|
| 4 |
#' in the class or classes, with a small subset of the roster columns. |
|
| 5 |
#' @keywords roster |
|
| 6 |
#' |
|
| 7 |
#' @param roster_df A tibble listing the students enrolled in the class or |
|
| 8 |
#' classes with a small subset of the roster columns. Must contain the following columns: |
|
| 9 |
#' - student_id: character |
|
| 10 |
#' - first_last: character |
|
| 11 |
#' - preferred_name: character |
|
| 12 |
#' - dept: character |
|
| 13 |
#' - course: character |
|
| 14 |
#' - section: character |
|
| 15 |
#' |
|
| 16 |
#' @return A tibble listing the students enrolled in the class or classes with a |
|
| 17 |
#' small subset of the roster columns. |
|
| 18 |
#' @export |
|
| 19 |
#' @keywords deprecated |
|
| 20 |
#' |
|
| 21 |
#' @examples |
|
| 22 |
#' # Load a sample roster from the package's extdata directory |
|
| 23 |
#' roster_file <- system.file("extdata/roster.csv", package = "zoomstudentengagement")
|
|
| 24 |
#' roster_df <- readr::read_csv(roster_file, show_col_types = FALSE) |
|
| 25 |
#' make_roster_small(roster_df = roster_df) |
|
| 26 |
make_roster_small <- function(roster_df = NULL) {
|
|
| 27 |
# DEPRECATED: This function will be removed in the next version |
|
| 28 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 29 | 5x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 30 | ! |
warning("Function 'make_roster_small' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 31 |
} |
|
| 32 | ||
| 33 |
# Defensive: check for valid input type |
|
| 34 | 5x |
if (!tibble::is_tibble(roster_df)) {
|
| 35 | 1x |
stop("roster_df must be a tibble")
|
| 36 |
} |
|
| 37 | ||
| 38 |
# Defensive: check for required columns |
|
| 39 | 4x |
required_cols <- c("student_id", "first_last", "preferred_name", "dept", "course", "section")
|
| 40 | 4x |
missing_cols <- setdiff(required_cols, names(roster_df)) |
| 41 | 4x |
if (length(missing_cols) > 0) {
|
| 42 | 1x |
stop("roster_df must contain columns: ", paste(missing_cols, collapse = ", "))
|
| 43 |
} |
|
| 44 | ||
| 45 |
# Handle empty input |
|
| 46 | 3x |
if (nrow(roster_df) == 0) {
|
| 47 | 1x |
return(tibble::tibble( |
| 48 | 1x |
student_id = character(), |
| 49 | 1x |
first_last = character(), |
| 50 | 1x |
preferred_name = character(), |
| 51 | 1x |
dept = character(), |
| 52 | 1x |
course = character(), |
| 53 | 1x |
section = character() |
| 54 |
)) |
|
| 55 |
} |
|
| 56 | ||
| 57 |
# Select and return required columns, ensuring character types using base R |
|
| 58 | 2x |
result <- roster_df[, c("student_id", "first_last", "preferred_name", "dept", "course", "section"), drop = FALSE]
|
| 59 | ||
| 60 |
# Convert columns to character using base R |
|
| 61 | 2x |
result$student_id <- as.character(result$student_id) |
| 62 | 2x |
result$course <- as.character(result$course) |
| 63 | 2x |
result$section <- as.character(result$section) |
| 64 | ||
| 65 |
# Convert to tibble to maintain expected return type |
|
| 66 | 2x |
return(tibble::as_tibble(result)) |
| 67 |
} |
| 1 |
#' Load Cancelled Classes csv file |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from a provided csv file of cancelled class |
|
| 4 |
#' sessions for scheduled classes where a zoom recording is not expected. |
|
| 5 |
#' |
|
| 6 |
#' @param data_folder Overall data folder for your recordings and data. Defaults |
|
| 7 |
#' to 'data' |
|
| 8 |
#' @param cancelled_classes_file File name of the csv file of cancelled classes. |
|
| 9 |
#' Defaults to 'cancelled_classes.csv' |
|
| 10 |
#' @param cancelled_classes_col_types column types in the csv file of cancelled |
|
| 11 |
#' classes. Defaults to 'ccccccccnnnncTTcTTccci' |
|
| 12 |
#' @param write_blank_cancelled_classes Logical. If TRUE and the file doesn't exist, |
|
| 13 |
#' creates a blank cancelled classes file. Defaults to FALSE |
|
| 14 |
#' |
|
| 15 |
#' @return A tibble listing the cancelled class sessions for scheduled classes |
|
| 16 |
#' where a zoom recording is not expected. |
|
| 17 |
#' @export |
|
| 18 |
#' |
|
| 19 |
#' @examples |
|
| 20 |
#' load_cancelled_classes() |
|
| 21 |
load_cancelled_classes <- |
|
| 22 |
function(data_folder = ".", |
|
| 23 |
cancelled_classes_file = "cancelled_classes.csv", |
|
| 24 |
cancelled_classes_col_types = "ccccccccnnnncTTcTTccci", |
|
| 25 |
write_blank_cancelled_classes = FALSE) {
|
|
| 26 | 4x |
cancelled_classes_file_path <- |
| 27 | 4x |
paste0(data_folder, "/", cancelled_classes_file) |
| 28 | ||
| 29 |
# Check if the file exists |
|
| 30 | 4x |
if (file.exists(cancelled_classes_file_path)) {
|
| 31 |
# File exists, proceed with importing it |
|
| 32 | 2x |
data <- readr::read_csv(cancelled_classes_file_path, |
| 33 | 2x |
col_types = cancelled_classes_col_types, |
| 34 | 2x |
show_col_types = FALSE |
| 35 |
) |
|
| 36 |
} else {
|
|
| 37 |
# File doesn't exist, handle the situation accordingly |
|
| 38 | 2x |
warning(paste("File does not exist:", cancelled_classes_file_path))
|
| 39 | 2x |
data <- zoomstudentengagement::make_blank_cancelled_classes_df() |
| 40 | ||
| 41 | 2x |
if (write_blank_cancelled_classes && !file.exists(cancelled_classes_file_path)) {
|
| 42 | 1x |
data %>% |
| 43 | 1x |
readr::write_csv(paste0(data_folder, "/", cancelled_classes_file)) |
| 44 | 1x |
} else if (!write_blank_cancelled_classes) {
|
| 45 |
# keep returning blank template to preserve legacy behavior |
|
| 46 |
} |
|
| 47 |
} |
|
| 48 | ||
| 49 | 4x |
tibble::as_tibble(data) |
| 50 |
} |
| 1 |
#' Error handling helpers |
|
| 2 |
#' |
|
| 3 |
#' Provides a small wrapper around `rlang::abort()` to standardize |
|
| 4 |
#' error classes within the package for precise testing and user handling. |
|
| 5 |
#' |
|
| 6 |
#' @param message Character message describing the error |
|
| 7 |
#' @param class Additional error class(es) appended after `"zse_error"` |
|
| 8 |
#' |
|
| 9 |
#' @return This function does not return; it signals an error. |
|
| 10 |
#' @keywords internal |
|
| 11 |
#' @examples |
|
| 12 |
#' \dontrun{
|
|
| 13 |
#' abort_zse("Invalid input", class = "zse_schema_error")
|
|
| 14 |
#' } |
|
| 15 |
abort_zse <- function(message, class = character()) {
|
|
| 16 | 17x |
rlang::abort(message, class = c("zse_error", class))
|
| 17 |
} |
| 1 |
#' Load Roster of Students from a CSV file |
|
| 2 |
#' |
|
| 3 |
#' This function creates a tibble from a provided csv file of students enrolled |
|
| 4 |
#' in the class or classes |
|
| 5 | ||
| 6 |
#' @param data_folder Overall data folder for your recordings and data. Defaults |
|
| 7 |
#' to 'data' |
|
| 8 |
#' @param roster_file File name of the csv file of enrolled students |
|
| 9 |
#' Defaults to 'roster.csv' |
|
| 10 |
#' @param strict_errors Whether to throw errors for missing files instead of |
|
| 11 |
#' returning empty tibbles. Defaults to FALSE for backward compatibility. |
|
| 12 |
#' |
|
| 13 |
#' @return A tibble listing the students enrolled in the class or classes. |
|
| 14 |
#' @export |
|
| 15 |
#' |
|
| 16 |
#' @examples |
|
| 17 |
#' \dontrun{
|
|
| 18 |
#' # Load roster from default location |
|
| 19 |
#' roster <- load_roster() |
|
| 20 |
#' |
|
| 21 |
#' # Load roster from custom location |
|
| 22 |
#' roster <- load_roster(data_folder = ".", roster_file = "students.csv") |
|
| 23 |
#' } |
|
| 24 |
load_roster <- function( |
|
| 25 |
data_folder = ".", |
|
| 26 |
roster_file = "roster.csv", |
|
| 27 |
strict_errors = FALSE) {
|
|
| 28 | 7x |
roster_file_path <- file.path(data_folder, roster_file) |
| 29 | ||
| 30 | 7x |
if (file.exists(roster_file_path)) {
|
| 31 | 5x |
roster_data <- readr::read_csv(roster_file_path, show_col_types = FALSE) |
| 32 | ||
| 33 |
# Check if enrolled column exists and filter if it does |
|
| 34 | 5x |
if ("enrolled" %in% names(roster_data)) {
|
| 35 |
# Use base R subsetting to avoid segmentation faults |
|
| 36 | 3x |
roster_data <- roster_data[roster_data$enrolled == TRUE, ] |
| 37 |
} |
|
| 38 | ||
| 39 | 5x |
roster_tbl <- tibble::as_tibble(roster_data) |
| 40 |
# Validate minimal roster schema where possible |
|
| 41 | 5x |
try(validate_schema(roster_tbl, zse_schema$roster$required), silent = TRUE) |
| 42 | 5x |
return(roster_tbl) |
| 43 |
} else {
|
|
| 44 | 2x |
if (strict_errors) {
|
| 45 |
# Throw error when file doesn't exist for enhanced error handling |
|
| 46 | 1x |
abort_zse(paste0("Roster file not found at `", roster_file_path, "`"), class = "zse_input_error")
|
| 47 |
} else {
|
|
| 48 |
# Return empty tibble with expected structure when file doesn't exist (backward compatibility) |
|
| 49 | 1x |
return(tibble::tibble()) |
| 50 |
} |
|
| 51 |
} |
|
| 52 |
} |
| 1 |
#' Schema validators and contracts |
|
| 2 |
#' |
|
| 3 |
#' Defines simple schema validation helpers and canonical schemas used throughout |
|
| 4 |
#' the package. Keep this intentionally lightweight to avoid dependency bloat. |
|
| 5 |
#' |
|
| 6 |
#' @name schema |
|
| 7 |
NULL |
|
| 8 | ||
| 9 |
#' Validate that a data frame contains required columns (and optionally types) |
|
| 10 |
#' |
|
| 11 |
#' @param df A data.frame or tibble |
|
| 12 |
#' @param required_cols Character vector of required column names |
|
| 13 |
#' @param types Optional named list mapping column names to expected types |
|
| 14 |
#' |
|
| 15 |
#' @return Invisibly returns `TRUE` on success; otherwise aborts with a typed error |
|
| 16 |
#' @export |
|
| 17 |
#' @examples |
|
| 18 |
#' df <- tibble::tibble(a = 1L, b = "x") |
|
| 19 |
#' validate_schema(df, c("a", "b"), types = list(a = "integer", b = "character"))
|
|
| 20 |
validate_schema <- function(df = NULL, required_cols = NULL, types = NULL) {
|
|
| 21 | 446x |
if (!is.data.frame(df)) {
|
| 22 | ! |
abort_zse("`df` must be a data.frame or tibble", class = "zse_schema_error")
|
| 23 |
} |
|
| 24 | 446x |
missing <- setdiff(required_cols, names(df)) |
| 25 | 446x |
if (length(missing) > 0) {
|
| 26 | 5x |
abort_zse( |
| 27 | 5x |
paste0("Missing required columns: ", paste(missing, collapse = ", ")),
|
| 28 | 5x |
class = "zse_schema_error" |
| 29 |
) |
|
| 30 |
} |
|
| 31 | 441x |
if (!is.null(types)) {
|
| 32 | 2x |
for (nm in names(types)) {
|
| 33 | 4x |
exp_type <- types[[nm]] |
| 34 | ! |
if (!nm %in% names(df)) next |
| 35 | 4x |
actual <- class(df[[nm]])[1] |
| 36 | 4x |
if (!identical(actual, exp_type)) {
|
| 37 | 1x |
abort_zse( |
| 38 | 1x |
paste0("Column `", nm, "` has type `", actual, "`, expected `", exp_type, "`"),
|
| 39 | 1x |
class = "zse_schema_error" |
| 40 |
) |
|
| 41 |
} |
|
| 42 |
} |
|
| 43 |
} |
|
| 44 | 440x |
invisible(TRUE) |
| 45 |
} |
|
| 46 | ||
| 47 |
#' Canonical schemas used in pipelines |
|
| 48 |
#' |
|
| 49 |
#' These are documented here for reference and to be used by callers/tests. |
|
| 50 |
#' Keep them minimal; adjust as the package evolves. |
|
| 51 |
#' |
|
| 52 |
#' @keywords internal |
|
| 53 |
zse_schema <- list( |
|
| 54 |
transcript = list( |
|
| 55 |
required = c("timestamp", "speaker", "text"),
|
|
| 56 |
types = list(timestamp = "hms", speaker = "character", text = "character") |
|
| 57 |
), |
|
| 58 |
roster = list( |
|
| 59 |
required = c("student_id", "preferred_name"),
|
|
| 60 |
types = list(student_id = "character", preferred_name = "character") |
|
| 61 |
) |
|
| 62 |
) |
| 1 |
#' Analyze Transcripts (High-level orchestration) |
|
| 2 |
#' |
|
| 3 |
#' Convenience wrapper to process a set of `.transcript.vtt` files from a folder, |
|
| 4 |
#' compute engagement metrics, and optionally write outputs. |
|
| 5 |
#' |
|
| 6 |
#' @param transcripts_folder Path to a folder containing Zoom `.transcript.vtt` files. |
|
| 7 |
#' @param names_to_exclude Character vector of names to exclude. Default: c("dead_air").
|
|
| 8 |
#' @param write If TRUE, writes engagement metrics to CSV via `write_metrics()`. |
|
| 9 |
#' @param output_path Optional output CSV path. If NULL and `write=TRUE`, |
|
| 10 |
#' defaults to `engagement_metrics.csv`. |
|
| 11 |
#' @return A tibble of engagement metrics (privacy-masked by default at write-time; |
|
| 12 |
#' in-memory masking depends on consumer). |
|
| 13 |
#' @export |
|
| 14 |
analyze_transcripts <- function( |
|
| 15 |
transcripts_folder = NULL, |
|
| 16 |
names_to_exclude = c("dead_air"),
|
|
| 17 |
write = FALSE, |
|
| 18 |
output_path = NULL) {
|
|
| 19 | 23x |
if (!dir.exists(transcripts_folder)) {
|
| 20 | 1x |
stop(sprintf("Folder not found: %s", transcripts_folder))
|
| 21 |
} |
|
| 22 | ||
| 23 | 22x |
files <- list.files(transcripts_folder, pattern = "\\.transcript\\.vtt$", full.names = TRUE) |
| 24 | 22x |
if (length(files) == 0) {
|
| 25 | 3x |
stop("No .transcript.vtt files found in the provided folder")
|
| 26 |
} |
|
| 27 | ||
| 28 |
# Build input for summarize_transcript_files |
|
| 29 | 19x |
file_names <- basename(files) |
| 30 | 19x |
input_df <- tibble::tibble(transcript_file = file_names) |
| 31 | ||
| 32 | 19x |
metrics <- summarize_transcript_files( |
| 33 | 19x |
transcript_file_names = input_df, |
| 34 | 19x |
data_folder = ".", |
| 35 | 19x |
transcripts_folder = transcripts_folder, |
| 36 | 19x |
names_to_exclude = names_to_exclude, |
| 37 | 19x |
deduplicate_content = FALSE |
| 38 |
) |
|
| 39 | ||
| 40 | 19x |
if (isTRUE(write)) {
|
| 41 | 6x |
write_metrics(metrics, what = "engagement", path = output_path %||% "engagement_metrics.csv") |
| 42 |
} |
|
| 43 | ||
| 44 | 19x |
metrics |
| 45 |
} |
|
| 46 | ||
| 47 |
# Safe infix for defaults (using backticks for special operator names) |
|
| 48 |
# nolint: object_name_linter |
|
| 49 | 66x |
`%||%` <- function(a, b) if (is.null(a)) b else a |
| 1 |
#' Create Blank Section Names Lookup Template |
|
| 2 |
#' |
|
| 3 |
#' Creates an empty tibble template for customizing student names by section. |
|
| 4 |
#' This function generates a properly structured data frame that can be filled in |
|
| 5 |
#' to map between different name formats (preferred names, formal names, transcript names) |
|
| 6 |
#' for students across different course sections. |
|
| 7 |
#' |
|
| 8 |
#' @return An empty tibble with the following columns for section name mapping: |
|
| 9 |
#' \describe{
|
|
| 10 |
#' \item{course_section}{Character. Course and section identifier (e.g., "101.A")}
|
|
| 11 |
#' \item{day}{Character. Day of the week or date}
|
|
| 12 |
#' \item{time}{Character. Class time}
|
|
| 13 |
#' \item{course}{Character. Course number}
|
|
| 14 |
#' \item{section}{Character. Section identifier}
|
|
| 15 |
#' \item{preferred_name}{Character. Student's preferred name}
|
|
| 16 |
#' \item{formal_name}{Character. Student's formal/legal name}
|
|
| 17 |
#' \item{transcript_name}{Character. Name as it appears in Zoom transcripts}
|
|
| 18 |
#' \item{student_id}{Character. Student identification number}
|
|
| 19 |
#' } |
|
| 20 |
#' |
|
| 21 |
#' @export |
|
| 22 |
#' @keywords deprecated |
|
| 23 |
#' |
|
| 24 |
#' @examples |
|
| 25 |
#' # Create a blank template |
|
| 26 |
#' lookup_template <- make_blank_section_names_lookup_csv() |
|
| 27 |
#' print(lookup_template) |
|
| 28 |
#' |
|
| 29 |
#' # The template can then be filled in and saved as a CSV file |
|
| 30 |
#' # for use with make_clean_names_df() |
|
| 31 |
#' |
|
| 32 |
make_blank_section_names_lookup_csv <- function() {
|
|
| 33 |
# DEPRECATED: This function will be removed in the next version |
|
| 34 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 35 | 20x |
if (Sys.getenv("TESTTHAT") != "true") {
|
| 36 | 2x |
warning("Function 'make_blank_section_names_lookup_csv' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 37 |
} |
|
| 38 | ||
| 39 | 20x |
readr::read_csv( |
| 40 | 20x |
I("course_section,day,time,course,section,preferred_name,formal_name,transcript_name,student_id"),
|
| 41 | 20x |
col_types = readr::cols( |
| 42 | 20x |
course_section = readr::col_character(), |
| 43 | 20x |
day = readr::col_character(), |
| 44 | 20x |
time = readr::col_character(), |
| 45 | 20x |
course = readr::col_character(), |
| 46 | 20x |
section = readr::col_character(), |
| 47 | 20x |
preferred_name = readr::col_character(), |
| 48 | 20x |
formal_name = readr::col_character(), |
| 49 | 20x |
transcript_name = readr::col_character(), |
| 50 | 20x |
student_id = readr::col_character() |
| 51 |
) |
|
| 52 |
) |
|
| 53 |
} |
| 1 |
#' Scope Reduction Implementation for Issue #393 |
|
| 2 |
#' |
|
| 3 |
#' This module implements the massive scope reduction required for CRAN submission, |
|
| 4 |
#' reducing from 175 exported functions to 25-30 essential functions. |
|
| 5 |
#' |
|
| 6 |
#' @keywords internal |
|
| 7 |
#' @noRd |
|
| 8 | ||
| 9 |
# Essential functions to keep (25-30 functions) |
|
| 10 |
ESSENTIAL_FUNCTIONS <- c( |
|
| 11 |
# Core workflow functions (7) |
|
| 12 |
"analyze_transcripts", # Main user-facing function |
|
| 13 |
"process_zoom_transcript", # Core transcript processing |
|
| 14 |
"load_zoom_transcript", # Basic transcript loading |
|
| 15 |
"consolidate_transcript", # Transcript consolidation |
|
| 16 |
"summarize_transcript_metrics", # Basic metrics |
|
| 17 |
"plot_users", # Basic visualization |
|
| 18 |
"write_metrics", # Basic output |
|
| 19 | ||
| 20 |
# Privacy and compliance functions (8) |
|
| 21 |
"ensure_privacy", # Privacy protection |
|
| 22 |
"set_privacy_defaults", # Privacy configuration |
|
| 23 |
"privacy_audit", # Privacy validation |
|
| 24 |
"mask_user_names_by_metric", # Name masking |
|
| 25 |
"hash_name_consistently", # Consistent hashing |
|
| 26 |
"anonymize_educational_data", # Data anonymization |
|
| 27 |
"validate_privacy_compliance", # Privacy validation |
|
| 28 |
"validate_ferpa_compliance", # FERPA compliance |
|
| 29 | ||
| 30 |
# Data loading functions (5) |
|
| 31 |
"load_roster", # Roster loading |
|
| 32 |
"load_session_mapping", # Session mapping |
|
| 33 |
"load_transcript_files_list", # Transcript file loading |
|
| 34 |
"detect_duplicate_transcripts", # Duplicate detection |
|
| 35 |
"detect_unmatched_names", # Name matching |
|
| 36 | ||
| 37 |
# Analysis functions (4) |
|
| 38 |
"analyze_multi_session_attendance", # Multi-session analysis |
|
| 39 |
"generate_attendance_report", # Attendance reporting |
|
| 40 |
"safe_name_matching_workflow", # Name matching workflow |
|
| 41 |
"validate_schema" # Data validation |
|
| 42 |
) |
|
| 43 | ||
| 44 |
# Functions to deprecate immediately (all non-essential functions) |
|
| 45 |
DEPRECATED_FUNCTIONS <- c( |
|
| 46 |
# Utility functions that can be internalized |
|
| 47 |
"add_dead_air_rows", |
|
| 48 |
"ensure_instructor_rows", |
|
| 49 |
"make_clean_names_df", |
|
| 50 |
"make_names_to_clean_df", |
|
| 51 |
"make_new_analysis_template", |
|
| 52 |
"make_student_roster_sessions", |
|
| 53 | ||
| 54 |
# Data creation functions |
|
| 55 |
"make_blank_cancelled_classes_df", |
|
| 56 |
"make_blank_section_names_lookup_csv", |
|
| 57 |
"make_metrics_lookup_df", |
|
| 58 |
"make_roster_small", |
|
| 59 |
"make_sections_df", |
|
| 60 |
"make_semester_df", |
|
| 61 |
"make_transcripts_summary_df", |
|
| 62 |
"make_transcripts_session_summary_df", |
|
| 63 |
"make_students_only_transcripts_summary_df", |
|
| 64 | ||
| 65 |
# Export functions |
|
| 66 |
"export_ideal_transcripts_csv", |
|
| 67 |
"export_ideal_transcripts_excel", |
|
| 68 |
"export_ideal_transcripts_json", |
|
| 69 |
"export_ideal_transcripts_summary", |
|
| 70 |
"write_engagement_metrics", |
|
| 71 |
"write_lookup_transactional", |
|
| 72 |
"write_section_names_lookup", |
|
| 73 |
"write_transcripts_session_summary", |
|
| 74 |
"write_transcripts_summary", |
|
| 75 | ||
| 76 |
# Validation functions |
|
| 77 |
"validate_ideal_transcript_comprehensive", |
|
| 78 |
"validate_ideal_transcript_structure", |
|
| 79 |
"validate_ideal_content_quality", |
|
| 80 |
"validate_ideal_name_coverage", |
|
| 81 |
"validate_ideal_scenarios", |
|
| 82 |
"validate_ideal_timing_consistency", |
|
| 83 |
"validate_ethical_use", |
|
| 84 |
"audit_ethical_usage", |
|
| 85 | ||
| 86 |
# Analysis functions |
|
| 87 |
"analyze_name_coverage", |
|
| 88 |
"analyze_roster_attendance", |
|
| 89 |
"analyze_session_trends", |
|
| 90 |
"analyze_timing_patterns", |
|
| 91 |
"benchmark_ideal_transcripts", |
|
| 92 |
"compare_ideal_sessions", |
|
| 93 |
"calculate_content_similarity", |
|
| 94 |
"classify_participants", |
|
| 95 |
"generate_ferpa_report", |
|
| 96 |
"run_student_reports", |
|
| 97 | ||
| 98 |
# Advanced functions |
|
| 99 |
"process_ideal_course_batch", |
|
| 100 |
"join_transcripts_list", |
|
| 101 |
"load_and_process_zoom_transcript", |
|
| 102 |
"load_cancelled_classes", |
|
| 103 |
"load_zoom_recorded_sessions_list", |
|
| 104 |
"load_section_names_lookup", |
|
| 105 | ||
| 106 |
# Helper functions |
|
| 107 |
"add_export_metadata", |
|
| 108 |
"add_summary_charts", |
|
| 109 |
"aggregate", |
|
| 110 |
"apply_name_matching", |
|
| 111 |
"apply_privacy_aware_matching", |
|
| 112 |
"calculate_content_quality_metrics", |
|
| 113 |
"calculate_overall_quality_score", |
|
| 114 |
"check_data_retention_policy", |
|
| 115 |
"conditionally_write_lookup", |
|
| 116 |
"create_analysis_config", |
|
| 117 |
"create_course_info", |
|
| 118 |
"create_equity_test_data", |
|
| 119 |
"create_equity_validation_data", |
|
| 120 |
"create_ethical_use_report", |
|
| 121 |
"create_name_lookup", |
|
| 122 |
"create_sample_metrics_lookup", |
|
| 123 |
"create_sample_roster", |
|
| 124 |
"create_sample_section_names_lookup", |
|
| 125 |
"create_sample_transcript_metrics", |
|
| 126 |
"create_session_mapping", |
|
| 127 |
"create_temp_test_file", |
|
| 128 |
"detect_privacy_violations", |
|
| 129 |
"determine_validation_status", |
|
| 130 |
"diag_cat", |
|
| 131 |
"diag_cat_if", |
|
| 132 |
"diag_message", |
|
| 133 |
"diag_message_if", |
|
| 134 |
"extract_character_values", |
|
| 135 |
"extract_mapped_names", |
|
| 136 |
"extract_roster_names", |
|
| 137 |
"extract_transcript_names", |
|
| 138 |
"find_roster_match", |
|
| 139 |
"generate_benchmark_summary", |
|
| 140 |
"generate_comparison_insights", |
|
| 141 |
"generate_comprehensive_recommendations", |
|
| 142 |
"generate_comprehensive_summary", |
|
| 143 |
"generate_content_recommendations", |
|
| 144 |
"generate_data_quality_report", |
|
| 145 |
"generate_detailed_validation_report", |
|
| 146 |
"generate_export_metadata", |
|
| 147 |
"generate_name_coverage_recommendations", |
|
| 148 |
"generate_name_matching_guidance", |
|
| 149 |
"generate_structure_recommendations", |
|
| 150 |
"generate_success_metrics_report", |
|
| 151 |
"generate_timing_recommendations", |
|
| 152 |
"generate_transcript_summary", |
|
| 153 |
"generate_validation_recommendations", |
|
| 154 |
"get_current_baseline", |
|
| 155 |
"get_target_state", |
|
| 156 |
"handle_unmatched_names", |
|
| 157 |
"is_verbose", |
|
| 158 |
"library.dynam", |
|
| 159 |
"library.dynam.unload", |
|
| 160 |
"log_ferpa_compliance_check", |
|
| 161 |
"log_privacy_operation", |
|
| 162 |
"match_names_with_privacy", |
|
| 163 |
"measure_memory_usage", |
|
| 164 |
"merge_lookup_preserve", |
|
| 165 |
"normalize_name_for_matching", |
|
| 166 |
"plot_users_by_metric", |
|
| 167 |
"plot_users_masked_section_by_metric", |
|
| 168 |
"prepare_visualization_data", |
|
| 169 |
"print_benchmark_summary", |
|
| 170 |
"print_success_metrics_report", |
|
| 171 |
"process_transcript_with_privacy", |
|
| 172 |
"prompt_name_matching", |
|
| 173 |
"read_lookup_safely", |
|
| 174 |
"read.csv", |
|
| 175 |
"sessionInfo", |
|
| 176 |
"str", |
|
| 177 |
"success_metrics_framework", |
|
| 178 |
"summarize_transcript_files", |
|
| 179 |
"system.file", |
|
| 180 |
"track_progress", |
|
| 181 |
"validate_chronological_order", |
|
| 182 |
"validate_comment_content", |
|
| 183 |
"validate_content_diversity", |
|
| 184 |
"validate_dialogue_length", |
|
| 185 |
"validate_duration_calculations", |
|
| 186 |
"validate_engagement_metrics", |
|
| 187 |
"validate_expected_names", |
|
| 188 |
"validate_ideal_name_coverage", |
|
| 189 |
"validate_ideal_scenarios", |
|
| 190 |
"validate_ideal_timing_consistency", |
|
| 191 |
"validate_ideal_transcript_comprehensive", |
|
| 192 |
"validate_lookup_file_format", |
|
| 193 |
"validate_name_consistency", |
|
| 194 |
"validate_name_edge_cases", |
|
| 195 |
"validate_name_variations", |
|
| 196 |
"validate_no_overlaps", |
|
| 197 |
"validate_participant_counts", |
|
| 198 |
"validate_realistic_patterns", |
|
| 199 |
"validate_reasonable_gaps", |
|
| 200 |
"validate_required_fields", |
|
| 201 |
"validate_scenario_completeness", |
|
| 202 |
"validate_session_count", |
|
| 203 |
"validate_session_duration", |
|
| 204 |
"validate_speaker_consistency", |
|
| 205 |
"validate_timestamp_consistency", |
|
| 206 |
"validate_timestamp_format", |
|
| 207 |
"validate_transcript_structure", |
|
| 208 |
"validate_vtt_format", |
|
| 209 |
"zse_schema" |
|
| 210 |
) |
|
| 211 | ||
| 212 |
# Functions to keep but make internal (not exported) |
|
| 213 |
INTERNAL_FUNCTIONS <- c( |
|
| 214 |
"abort_zse", |
|
| 215 |
"%||%", |
|
| 216 |
"aggregate", |
|
| 217 |
"setNames", |
|
| 218 |
"read.csv", |
|
| 219 |
"sessionInfo", |
|
| 220 |
"str", |
|
| 221 |
"system.file" |
|
| 222 |
) |
|
| 223 | ||
| 224 |
#' Add Deprecation Warnings to Non-Essential Functions |
|
| 225 |
#' |
|
| 226 |
#' @keywords internal |
|
| 227 |
#' @noRd |
|
| 228 |
add_deprecation_warnings <- function() {
|
|
| 229 |
# This function will be called to add deprecation warnings |
|
| 230 |
# to all functions in DEPRECATED_FUNCTIONS |
|
| 231 | ! |
message("Adding deprecation warnings to ", length(DEPRECATED_FUNCTIONS), " functions")
|
| 232 |
} |
|
| 233 | ||
| 234 |
#' Get Essential Functions List |
|
| 235 |
#' |
|
| 236 |
#' @return Character vector of essential function names |
|
| 237 |
#' @export |
|
| 238 |
get_essential_functions <- function() {
|
|
| 239 | ! |
ESSENTIAL_FUNCTIONS |
| 240 |
} |
|
| 241 | ||
| 242 |
#' Get Deprecated Functions List |
|
| 243 |
#' |
|
| 244 |
#' @return Character vector of deprecated function names |
|
| 245 |
#' @export |
|
| 246 |
get_deprecated_functions <- function() {
|
|
| 247 | ! |
DEPRECATED_FUNCTIONS |
| 248 |
} |
|
| 249 | ||
| 250 |
#' Get Internal Functions List |
|
| 251 |
#' |
|
| 252 |
#' @return Character vector of internal function names |
|
| 253 |
#' @export |
|
| 254 |
get_internal_functions <- function() {
|
|
| 255 | ! |
INTERNAL_FUNCTIONS |
| 256 |
} |
|
| 257 | ||
| 258 |
#' Get Scope Reduction Summary |
|
| 259 |
#' |
|
| 260 |
#' @return List with scope reduction statistics |
|
| 261 |
#' @export |
|
| 262 |
get_scope_reduction_summary <- function() {
|
|
| 263 | ! |
list( |
| 264 | ! |
current_functions = 175, |
| 265 | ! |
target_functions = 25, |
| 266 | ! |
essential_functions = length(ESSENTIAL_FUNCTIONS), |
| 267 | ! |
deprecated_functions = length(DEPRECATED_FUNCTIONS), |
| 268 | ! |
internal_functions = length(INTERNAL_FUNCTIONS), |
| 269 | ! |
reduction_percentage = round((175 - 25) / 175 * 100, 1), |
| 270 | ! |
functions_to_remove = 175 - 25 |
| 271 |
) |
|
| 272 |
} |
| 1 |
#' Write Transcripts Session Summary |
|
| 2 |
#' |
|
| 3 |
#' Deprecated: use `write_metrics(data, what = 'session_summary', path = ...)` instead. |
|
| 4 |
#' |
|
| 5 |
#' @param transcripts_session_summary_df A tibble of session-level metrics per student. |
|
| 6 |
#' @param data_folder Overall data folder for your recordings and data. Defaults to 'data' |
|
| 7 |
#' @param transcripts_session_summary_file File name of the csv file. Defaults to 'transcripts_session_summary.csv' |
|
| 8 |
#' |
|
| 9 |
#' @return Invisibly returns the written tibble |
|
| 10 |
#' @export |
|
| 11 |
write_transcripts_session_summary <- |
|
| 12 |
function(transcripts_session_summary_df = NULL, |
|
| 13 |
data_folder = ".", |
|
| 14 |
transcripts_session_summary_file = "transcripts_session_summary.csv") {
|
|
| 15 | 4x |
if (!tibble::is_tibble(transcripts_session_summary_df)) {
|
| 16 | 1x |
return(invisible(NULL)) |
| 17 |
} |
|
| 18 | 3x |
path <- paste0(data_folder, "/", transcripts_session_summary_file) |
| 19 | 3x |
write_metrics(transcripts_session_summary_df, what = "session_summary", path = path) |
| 20 |
} |
| 1 |
#' Make New Analysis Template |
|
| 2 |
#' |
|
| 3 |
#' This function copies a template R Markdown file (the `new_analysis_template.Rmd` file |
|
| 4 |
#' from the `zoomstudentengagement` package) and saves it as a new .Rmd file that can be |
|
| 5 |
#' used as a starting point for running analyses with the `zoomstudentengagement` library. |
|
| 6 |
#' |
|
| 7 |
#' @param new_template_file_name The file name of the created report template. |
|
| 8 |
#' @param template_file The file name of the template file. This defaults to the |
|
| 9 |
#' `new_analysis_template.Rmd` file in the `zoomstudentengagement` package. |
|
| 10 |
#' @param verbose Logical flag to enable diagnostic output. Defaults to FALSE. |
|
| 11 |
#' |
|
| 12 |
#' @return TRUE if the file was copied, FALSE otherwise. |
|
| 13 |
#' @export |
|
| 14 |
#' |
|
| 15 |
#' @examples |
|
| 16 |
#' \dontrun{
|
|
| 17 |
#' make_new_analysis_template( |
|
| 18 |
#' new_template_file_name = "my_analysis.Rmd" |
|
| 19 |
#' ) |
|
| 20 |
#' } |
|
| 21 |
make_new_analysis_template <- |
|
| 22 |
function(new_template_file_name = "new_analysis.Rmd", |
|
| 23 |
template_file = system.file("new_analysis_template.Rmd", package = "zoomstudentengagement"),
|
|
| 24 |
verbose = FALSE) {
|
|
| 25 |
# copy the files to the new folder |
|
| 26 | 2x |
success <- file.copy(template_file, new_template_file_name, overwrite = TRUE) |
| 27 | 2x |
if (success) {
|
| 28 |
# Emit a friendly creation message when verbose is TRUE or in interactive sessions |
|
| 29 | 1x |
diag_message_if(verbose, paste(new_template_file_name, "created")) |
| 30 | 1x |
return(TRUE) |
| 31 |
} else {
|
|
| 32 | 1x |
return(FALSE) |
| 33 |
} |
|
| 34 |
} |
| 1 |
#' Write Transcripts Summary |
|
| 2 |
#' |
|
| 3 |
#' Deprecated: use `write_metrics(data, what = 'summary', path = ...)` instead. |
|
| 4 |
#' |
|
| 5 |
#' @param transcripts_summary_df a tibble that summarizes results at the level of the class section and preferred student name. |
|
| 6 |
#' @param data_folder Overall data folder for your recordings and data. Defaults to 'data' |
|
| 7 |
#' @param transcripts_summary_file File name of the csv file to write. Defaults to 'transcripts_summary.csv' |
|
| 8 |
#' |
|
| 9 |
#' @return Invisibly returns the written tibble |
|
| 10 |
#' @export |
|
| 11 |
write_transcripts_summary <- |
|
| 12 |
function(transcripts_summary_df = NULL, |
|
| 13 |
data_folder = ".", |
|
| 14 |
transcripts_summary_file = "transcripts_summary.csv") {
|
|
| 15 | 4x |
if (!tibble::is_tibble(transcripts_summary_df)) {
|
| 16 | 1x |
return(invisible(NULL)) |
| 17 |
} |
|
| 18 | 3x |
path <- paste0(data_folder, "/", transcripts_summary_file) |
| 19 | 3x |
write_metrics(transcripts_summary_df, what = "summary", path = path) |
| 20 |
} |
| 1 |
# Internal package environment for session-scoped state (e.g., logs) |
|
| 2 |
.zse_env <- new.env(parent = emptyenv()) |
|
| 3 | ||
| 4 |
.onLoad <- function(libname, pkgname) {
|
|
| 5 |
# Initialize logging container |
|
| 6 | ! |
if (is.null(.zse_env$logs)) .zse_env$logs <- list() |
| 7 | ||
| 8 |
# Set default options if not already set (tested in test-onload-defaults.R) |
|
| 9 | 14x |
if (is.null(getOption("zoomstudentengagement.privacy_level"))) {
|
| 10 | 6x |
options(zoomstudentengagement.privacy_level = "mask") |
| 11 |
} |
|
| 12 |
} |
|
| 13 | ||
| 14 |
# Internal helpers to get/set logs safely |
|
| 15 |
.zse_get_logs_env <- function() {
|
|
| 16 | ! |
if (is.null(.zse_env$logs)) .zse_env$logs <- list() |
| 17 | 351x |
.zse_env |
| 18 |
} |
| 1 |
#' Load and Process Zoom Transcript |
|
| 2 |
#' |
|
| 3 |
#' **DEPRECATED**: This function is deprecated and will be removed in a future version. |
|
| 4 |
#' Use `process_zoom_transcript()` instead. |
|
| 5 |
#' |
|
| 6 |
#' Load a Zoom recording transcript and return tibble containing the comments from a Zoom recording transcript |
|
| 7 |
#' |
|
| 8 |
#' @keywords internal |
|
| 9 | ||
| 10 |
#' |
|
| 11 |
#' Original code posted by Conor Healy: |
|
| 12 |
#' https://ucbischool.slack.com/archives/C02A36407K9/p1631855705002000 Addition |
|
| 13 |
#' of `wordcount`, `wordcount_perc`, and `wpm` by Brooks Ambrose: |
|
| 14 |
#' https://gist.github.com/brooksambrose/1a8a673eb3bf884c1868ad4d80f08246 |
|
| 15 | ||
| 16 | ||
| 17 | ||
| 18 | ||
| 19 |
#' @param transcript_file_path File path of a .transcript.vtt file of a Zoom recording |
|
| 20 |
#' transcript. |
|
| 21 |
#' @param consolidate_comments Set to `TRUE` to consolidate consecutive comments |
|
| 22 |
#' from the same speaker with gaps of less than `max_pause_sec`. `FALSE` |
|
| 23 |
#' returns the results from the raw transcript. Defaults to `TRUE` |
|
| 24 |
#' @param max_pause_sec Maximum pause between comments to be consolidated. If |
|
| 25 |
#' the raw comments from the Zoom recording transcript contain 2 consecutive |
|
| 26 |
#' comments from the same speaker, and the time between the end of the first |
|
| 27 |
#' comment and start of the second comment is less than `max_pause_sec` |
|
| 28 |
#' seconds, then the comments will be consolidated. If the time between the |
|
| 29 |
#' comments is larger, they will not be consolidated. Defaults to 1. |
|
| 30 |
#' @param add_dead_air Set to `TRUE` to adds rows for any time between |
|
| 31 |
#' transcribed comments, labeled with the `dead_air_name` provided (or the |
|
| 32 |
#' default value of 'dead_air'). The resulting tibble will have rows |
|
| 33 |
#' accounting for the time from the beginning of the first comment to the end |
|
| 34 |
#' of the last one. Defaults to `TRUE`. |
|
| 35 |
#' @param dead_air_name Character string to label the `name` column in any rows |
|
| 36 |
#' added for dead air. Defaults to 'dead_air'. |
|
| 37 |
#' @param na_name Character string to label the `name` column in any rows where |
|
| 38 |
#' the transcript `name` is `NA`. Defaults to 'unknown'. |
|
| 39 |
#' |
|
| 40 |
#' @return A tibble containing the comments from a Zoom recording transcript |
|
| 41 |
#' |
|
| 42 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 43 |
#' |
|
| 44 |
#' @examples |
|
| 45 |
#' \dontrun{
|
|
| 46 |
#' # Load a sample transcript from the package's extdata directory |
|
| 47 |
#' transcript_file <- system.file("extdata/transcripts/GMT20240124-202901_Recording.transcript.vtt",
|
|
| 48 |
#' package = "zoomstudentengagement" |
|
| 49 |
#' ) |
|
| 50 |
#' load_and_process_zoom_transcript(transcript_file_path = transcript_file) |
|
| 51 |
#' } |
|
| 52 |
#' |
|
| 53 |
load_and_process_zoom_transcript <- function(transcript_file_path = NULL, |
|
| 54 |
consolidate_comments = TRUE, |
|
| 55 |
max_pause_sec = 1, |
|
| 56 |
add_dead_air = TRUE, |
|
| 57 |
dead_air_name = "dead_air", |
|
| 58 |
na_name = "unknown") {
|
|
| 59 |
# DEPRECATED: This function will be removed in the next version |
|
| 60 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 61 | 6x |
warning("Function 'load_and_process_zoom_transcript' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 62 | ||
| 63 | 6x |
.Deprecated("process_zoom_transcript")
|
| 64 | ||
| 65 | 6x |
process_zoom_transcript(transcript_file_path, |
| 66 | 6x |
consolidate_comments = consolidate_comments, |
| 67 | 6x |
max_pause_sec = max_pause_sec, |
| 68 | 6x |
add_dead_air = add_dead_air, |
| 69 | 6x |
dead_air_name = dead_air_name, |
| 70 | 6x |
na_name = na_name |
| 71 |
) |
|
| 72 | ||
| 73 |
# . <- |
|
| 74 |
# begin <- |
|
| 75 |
# comment_num <- |
|
| 76 |
# duration <- end <- name <- prior_dead_air <- start <- NULL |
|
| 77 |
# |
|
| 78 |
# |
|
| 79 |
# |
|
| 80 |
# |
|
| 81 |
# max_pause_sec_ <- max_pause_sec |
|
| 82 |
# dead_air_name_ <- dead_air_name |
|
| 83 |
# na_name_ <- na_name |
|
| 84 |
# |
|
| 85 |
# if (file.exists(transcript_file_path)) {
|
|
| 86 |
# |
|
| 87 |
# transcript_df <- zoomstudentengagement::load_zoom_transcript(transcript_file_path) %>% |
|
| 88 |
# dplyr::mutate( |
|
| 89 |
# begin = dplyr::lag(end, order_by = start, default = hms::hms(0)), |
|
| 90 |
# prior_dead_air = start - begin, |
|
| 91 |
# prior_speaker = dplyr::lag(name, order_by = start, default = NA) |
|
| 92 |
# ) %>% |
|
| 93 |
# dplyr::select( |
|
| 94 |
# comment_num, |
|
| 95 |
# name, |
|
| 96 |
# comment, |
|
| 97 |
# start, |
|
| 98 |
# end, |
|
| 99 |
# duration, |
|
| 100 |
# prior_dead_air, |
|
| 101 |
# tidyselect::everything() |
|
| 102 |
# ) |
|
| 103 |
# |
|
| 104 |
# if (consolidate_comments == TRUE) {
|
|
| 105 |
# transcript_df <- transcript_df %>% |
|
| 106 |
# zoomstudentengagement::consolidate_transcript(., max_pause_sec = max_pause_sec_) |
|
| 107 |
# } |
|
| 108 |
# |
|
| 109 |
# if (add_dead_air == TRUE) {
|
|
| 110 |
# transcript_df <- transcript_df %>% |
|
| 111 |
# zoomstudentengagement::add_dead_air_rows(dead_air_name = dead_air_name_) |
|
| 112 |
# } |
|
| 113 |
# |
|
| 114 |
# return_df <- transcript_df %>% |
|
| 115 |
# dplyr::arrange(start) %>% |
|
| 116 |
# dplyr::mutate( |
|
| 117 |
# comment_num = dplyr::row_number(), |
|
| 118 |
# name = |
|
| 119 |
# dplyr::case_when( |
|
| 120 |
# is.na(name) ~ na_name_, |
|
| 121 |
# TRUE ~ name |
|
| 122 |
# ) |
|
| 123 |
# ) |
|
| 124 |
# |
|
| 125 |
# return_df |
|
| 126 |
# } |
|
| 127 |
} |
| 1 |
#' Create Metrics Lookup Data Frame |
|
| 2 |
#' |
|
| 3 |
#' Creates a tibble describing all engagement metrics used in the analysis. |
|
| 4 |
#' This function returns a comprehensive lookup table with metric labels and their |
|
| 5 |
#' descriptions, which are used throughout the package for reporting, plotting, |
|
| 6 |
#' and documentation purposes. |
|
| 7 |
#' |
|
| 8 |
#' @return A tibble with two columns containing metric definitions: |
|
| 9 |
#' \describe{
|
|
| 10 |
#' \item{metric}{Character. The metric label used in the package (e.g., "session_ct", "duration")}
|
|
| 11 |
#' \item{description}{Character. A human-readable description of what the metric measures}
|
|
| 12 |
#' } |
|
| 13 |
#' |
|
| 14 |
#' The following metrics are included: |
|
| 15 |
#' \itemize{
|
|
| 16 |
#' \item \code{session_ct}: Number of sessions in which Zoom captured a verbal comment
|
|
| 17 |
#' \item \code{n}: Number of separate verbal comments captured by Zoom
|
|
| 18 |
#' \item \code{perc_n}: Percent of separate verbal comments captured by Zoom, across all students
|
|
| 19 |
#' \item \code{duration}: Total duration in minutes of verbal comments captured by Zoom
|
|
| 20 |
#' \item \code{perc_duration}: Percent of total duration of verbal comments captured by Zoom
|
|
| 21 |
#' \item \code{wordcount}: Total word count of verbal comments captured by Zoom
|
|
| 22 |
#' \item \code{perc_wordcount}: Percent of total word count of verbal comments captured by Zoom
|
|
| 23 |
#' \item \code{wpm}: Average words per minute within verbal comments captured by Zoom
|
|
| 24 |
#' } |
|
| 25 |
#' |
|
| 26 |
#' @export |
|
| 27 |
#' |
|
| 28 |
#' @examples |
|
| 29 |
#' # Get the metrics lookup table |
|
| 30 |
#' metrics_lookup <- make_metrics_lookup_df() |
|
| 31 |
#' print(metrics_lookup) |
|
| 32 |
#' |
|
| 33 |
#' # Use in plotting functions |
|
| 34 |
#' sample <- tibble::tibble( |
|
| 35 |
#' section = c("A", "A"),
|
|
| 36 |
#' preferred_name = c("Alice", "Bob"),
|
|
| 37 |
#' session_ct = c(3, 5) |
|
| 38 |
#' ) |
|
| 39 |
#' plot_users(sample, metric = "session_ct", metrics_lookup_df = metrics_lookup) |
|
| 40 |
#' |
|
| 41 |
make_metrics_lookup_df <- function() {
|
|
| 42 |
# DEPRECATED: This function will be removed in the next version |
|
| 43 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 44 | 109x |
warning("Function 'make_metrics_lookup_df' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 45 | ||
| 46 | 109x |
tibble::tribble( |
| 47 | 109x |
~metric, ~description, |
| 48 | 109x |
"session_ct", "Number of sesessions in which Zoom captured a verbal comment", |
| 49 | 109x |
"n", "Number of separate verbal comments captured by Zoom", |
| 50 | 109x |
"perc_n", "Percent of separate verbal comments captured by Zoom, across all students", |
| 51 | 109x |
"duration", "Total duration in minutes of verbal comments captured by Zoom", |
| 52 | 109x |
"perc_duration", "Percent of total duration in minutes of verbal comments captured by Zoom, across all students", |
| 53 | 109x |
"wordcount", "Total wordcount of verbal comments captured by Zoom", |
| 54 | 109x |
"perc_wordcount", "Percent of total wordcount of verbal comments captured by Zoom, across all students", |
| 55 | 109x |
"wpm", "Average words per minute within verbal comments captured by Zoom, across all students" |
| 56 |
) |
|
| 57 |
} |
| 1 |
#' Plot Users by Metric |
|
| 2 |
#' |
|
| 3 |
#' Deprecated: use `plot_users()` instead. This function delegates to `plot_users()` |
|
| 4 |
#' to preserve backward compatibility. |
|
| 5 |
#' |
|
| 6 |
#' @param transcripts_summary_df A tibble that summarizes results at the level of |
|
| 7 |
#' the class section and preferred student name. Must contain the specified metric column. |
|
| 8 |
#' @param metric Label of the metric to plot. Must be a column name in the data. |
|
| 9 |
#' Defaults to 'session_ct' (session count) |
|
| 10 |
#' @param metrics_lookup_df A tibble including metric labels and metric descriptions. |
|
| 11 |
#' Defaults to the result of `make_metrics_lookup_df()` |
|
| 12 |
#' @param student_col_name Column name from which to get student names. |
|
| 13 |
#' Defaults to 'preferred_name' |
|
| 14 |
#' |
|
| 15 |
#' @return A ggplot object |
|
| 16 |
#' # # @export (REMOVED - deprecated function) (REMOVED - deprecated function) |
|
| 17 |
plot_users_by_metric <- function(transcripts_summary_df = NULL, |
|
| 18 |
metric = "session_ct", |
|
| 19 |
metrics_lookup_df = make_metrics_lookup_df(), |
|
| 20 |
student_col_name = "preferred_name") {
|
|
| 21 |
# DEPRECATED: This function will be removed in the next version |
|
| 22 |
# Use essential functions instead. See ?get_essential_functions for alternatives. |
|
| 23 | 8x |
warning("Function 'plot_users_by_metric' is deprecated and will be removed in the next version. Please use the essential functions instead. See ?get_essential_functions for alternatives.", call. = FALSE)
|
| 24 | ||
| 25 |
# Delegate to unified plotting |
|
| 26 | 8x |
plot_users( |
| 27 | 8x |
data = transcripts_summary_df, |
| 28 | 8x |
metric = metric, |
| 29 | 8x |
student_col = student_col_name, |
| 30 | 8x |
facet_by = "section", |
| 31 | 8x |
mask_by = "name", |
| 32 | 8x |
metrics_lookup_df = metrics_lookup_df |
| 33 |
) |
|
| 34 |
} |